CN118010014B - Multi-source heterogeneous information fusion positioning method for complex pipeline environment based on subgraph matching - Google Patents
Multi-source heterogeneous information fusion positioning method for complex pipeline environment based on subgraph matchingInfo
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Abstract
本发明公开了一种基于子图匹配的复杂管道环境多源异构信息融合定位方法。针对相似管道环境下的点云退化问题，使用扩展卡尔曼滤波方法利用预处理后的异构传感器信息构建机器人运动系统方程、测量方程，迭代更新卡尔曼增益并输出准确的局部先验位姿估计。针对传统定位算法累计误差随时间线性增长的问题，利用惯性测量单元数据对前置机械式激光雷达点云进行平面投影并构建子图，使用子图匹配思想紧耦合先验位姿与子图的局部约束因素从而估计机器人在滑动窗口内相邻数据帧间的运动增量，最终最小化所有因素残差以获得管道巡检机器人状态的最大后验估计。通过相应实验，验证了所提出定位方法的准确性与有效性，该方法能够有效减少管道巡检机器人在复杂狭窄管道环境下的定位误差，达到了厘米级的定位精度。
The present invention discloses a multi-source heterogeneous information fusion positioning method for complex pipeline environments based on subgraph matching. In order to solve the problem of point cloud degradation in similar pipeline environments, the extended Kalman filter method is used to construct the robot motion system equation and measurement equation using the preprocessed heterogeneous sensor information, iteratively update the Kalman gain and output accurate local prior pose estimation. In order to solve the problem that the cumulative error of the traditional positioning algorithm grows linearly with time, the inertial measurement unit data is used to perform planar projection on the front mechanical lidar point cloud and construct a subgraph. The subgraph matching idea is used to tightly couple the prior pose and the local constraint factors of the subgraph to estimate the robot's motion increment between adjacent data frames in the sliding window, and finally minimize the residuals of all factors to obtain the maximum a posteriori estimate of the pipeline inspection robot's state. The accuracy and effectiveness of the proposed positioning method are verified through corresponding experiments. The method can effectively reduce the positioning error of the pipeline inspection robot in a complex and narrow pipeline environment and achieve centimeter-level positioning accuracy.
Description
Technical Field
The invention belongs to the technical field of positioning of pipeline inspection robots, and particularly relates to a complex pipeline environment multi-source heterogeneous information fusion positioning method based on sub-graph matching.
Background
With the continuous increase of urban population, central air conditioning pipeline systems are becoming key devices for ventilation and humidity regulation of high-rise buildings. With the increase of the service time, the accumulated fungi, bacteria and other pollutants inside the pipeline seriously threaten the physical health of people in the building. Because of the complexity and diversity of the pipeline structure, the pipeline network of the whole building cannot be inspected manually, so that the pipeline inspection robot is generated. In order to acquire accurate and reliable positioning information, the existing pipeline inspection robot is usually required to be provided with heterogeneous sensor equipment such as a wheel encoder, an Inertial Measurement Unit (IMU), a laser radar and the like in the operation process, but the wheel encoder has measurement errors caused by skidding and uneven pipelines, has system errors caused by inaccurate kinematic modeling of the robot, the IMU has accumulated errors caused by zero offset, angle random walk and the like, and the laser radar has strong reflection conditions in the metal pipeline to cause point cloud distortion and is easy to generate point cloud degradation in long straight pipelines. And due to cost limitations and load constraints, small pipeline inspection robots cannot be equipped with excessively expensive high-precision positioning equipment.
Aiming at the problems, a real-time positioning method of the multi-source heterogeneous information fusion positioning method which is applicable to complex metal pipeline environments and does not depend on a high-precision sensor is urgently needed.
Disclosure of Invention
The invention aims to provide a multi-source heterogeneous information fusion positioning method based on an encoder, an IMU and a laser radar in a complex pipeline environment, which solves the problem that the errors of the encoder and the IMU are accumulated along with the running time and the point cloud of the laser radar is distorted in the metal pipeline environment, comprehensively utilizes the ideas of Extended Kalman Filtering (EKF) and sub-graph Matching (Subgraph-Matching) to fuse multi-sensor data, and realizes the real-time accurate positioning of a pipeline inspection robot in the complex pipeline environment.
The technical scheme for achieving the purpose of the invention is that on one hand, a complex pipeline environment multi-source heterogeneous information fusion positioning method based on sub-graph matching is provided, and the method comprises the following steps:
step 1, establishing a pipeline inspection robot system dynamics model;
Step 2, placing the pipeline inspection robot in a complex pipeline environment to acquire heterogeneous sensor data, wherein the sensor comprises an encoder, an Inertial Measurement Unit (IMU), a side solid-state laser radar and a front mechanical laser radar;
Step 3, preprocessing the collected heterogeneous sensor data to obtain measurement information of the pipeline inspection robot and the surrounding environment thereof;
step 4, constructing a motion system equation of the pipeline inspection robot according to the encoder data, and initializing the pose state and covariance of the pipeline inspection robot;
Step 5, constructing and updating a measurement equation according to the IMU and the side solid-state laser radar data, calculating Kalman gain based on an Extended Kalman Filter (EKF) method, and iteratively updating to obtain priori pose estimation of the pipeline inspection robot;
Step 6, carrying out plane projection on the front mechanical laser radar point cloud by using IMU data and constructing a subgraph;
and 7, estimating the motion increment between adjacent data frames of the pipeline inspection robot in the sliding window by using the sub-graph matching thought to tightly couple the priori pose and the local constraint of the sub-graph, and minimizing all factor residuals to solve the maximum posterior estimation of the state of the pipeline inspection robot.
Further, the system dynamics model of the pipeline inspection robot is built in the step 1, specifically:
Wherein v x,vy is the linear velocity of the motion along the x and y directions under the coordinate system of the pipeline inspection robot, ω is the angular velocity of the rotation of the coordinate system of the pipeline inspection robot relative to the global coordinate system, l is the track of the robot, Δψ is the variation of the yaw angle of the pipeline inspection robot at adjacent moments, ψ is the yaw angle of the pipeline inspection robot, and v l,vr is the linear velocity of the operation of the left and right wheels of the pipeline inspection robot.
Further, the step 2 of collecting heterogeneous sensor data comprises collecting data of left and right wheel encoders at time tTriaxial accelerometer data a x,ay,az, triaxial gyroscope data g x,gy,gz, triaxial magnetometer data m x,my,mz of IMU, point cloud data of side solid-state laser radarFront-mounted mechanical laser radar data
Further, the preprocessing of the collected heterogeneous sensor data in step 3 to obtain pose information of the pipeline inspection robot specifically includes:
Step 3-1, respectively calculating left and right wheel linear speeds v l,vr of the pipeline inspection robot according to encoder data, wherein the method specifically comprises the following steps:
wherein M is the count of the encoder when the wheel of the pipeline inspection robot rotates for a complete circle, Left and right wheel encoder data at time t respectively,The data of left and right wheel encoders at the time t+1 are respectively obtained, d is the diameter of a wheel of the pipeline inspection robot, deltat is the time difference of two samples, and v x,vy and omega can be calculated based on the speeds of the left and right wheels;
The pose change amounts Δx odom,Δyodom and Δψ odom of the pipe inspection robot within Δt calculated from the encoder data are then shown, concretely:
Accumulating the delta x odom,Δyodom and the delta phi odom to obtain the coded odometer pose of the robot at each moment;
Step 3-2, filtering the IMU original data, namely obtaining corresponding quaternion data by fusing data of a gyroscope, an accelerometer and a magnetometer through a six-axis IMU complementary filtering method, wherein the method specifically comprises the following steps of:
wherein, the Representing the change of the IMU coordinate system relative to the world coordinate system, q 0,q1,q2,q3 represents a quaternion used for representing the rotation angle of the robot in the three-dimensional space;
The quaternion calculated by IMU data also obtains corresponding triaxial Euler angles, namely a roll angle theta IMU, a pitch angle phi IMU and a yaw angle phi IMU;
step 3-3, performing least square fitting on point cloud data between +20 degrees of the side solid-state laser radar and-20 degrees of the side solid-state laser radar to obtain the distance between the side laser radar and the pipeline wall at the moment t
The coordinate offset delta y lidar of the robot in the y-axis direction at the adjacent time is obtained, specifically:
wherein I bool is a sign constant, I bool is +1 for the right laser radar and I bool is-1 for the left laser radar
Further, step 4, constructing a motion system equation of the pipeline inspection robot according to the encoder data, and initializing the pose state and covariance of the pipeline inspection robot, which specifically includes:
Defining a state quantity x= [ x y psi ] T, and constructing a system equation f by using a measured value of an encoder to perform prior estimation on the pose of the robot, wherein the control quantity u= [ vω ] T is specifically as follows:
wherein, the For an a priori estimate of the time instant k,For the best posterior estimation at the time of k-1, [ v k ωk]T ] is the control quantity at the time of k, v k,ωk is the linear speed and the angular speed of the motion of the pipeline robot at the time of k, w k is the process noise of the system model at the time of k, and the Gaussian distribution P (w): N (0, Q) is satisfied, wherein Q is the covariance matrix of Gaussian noise.
Further, in step 5, a measurement equation is constructed and updated according to the IMU and the side solid-state laser radar data, and a kalman gain is calculated and iteratively updated based on an Extended Kalman Filter (EKF) method, so as to obtain a priori pose of the pipeline inspection robot, which specifically includes:
step 5-1, respectively calculating covariance matrixes of priori errors of IMU at k moment and side solid-state laser radar AndThe method comprises the following steps:
wherein, the AndRepresenting the error covariance matrix of the IMU and the solid-state lidar at time k-1, respectively, A k representing the jacobian matrix of the partial derivative of the system equation f with respect to x, and W k representing the jacobian matrix of the partial derivative of the system equation f with respect to W, specifically:
Step 5-2, respectively calculating the Kalman gains corresponding to the IMU at the k moment and the side solid-state laser radar AndThe method comprises the following steps:
Wherein H represents a jacobian matrix of a partial derivative of the sensor observation function H with respect to x, V represents a jacobian matrix of a partial derivative of the sensor observation function H with respect to V, H 'and V' are transposes of H and V, respectively, σ represents measurement noise, a Gaussian distribution, P (σ): (0, R), R is a measurement noise covariance matrix, and the variables related to H and V described above are expressed as:
wherein, the AndThe observed functions are respectively corresponding to the IMU sensor and the solid-state laser radar sensor, and the observed values areAndThe method comprises the following steps:
wherein, the Representing the y-axis offset of the robot relative to the initial state obtained by the side lidar;
Step 5-3, calculating a posterior estimate using the obtained Kalman gain And
Step 5-4, updating the error covariance matrix of the IMU and the solid-state laser radarAndThe method comprises the following steps:
Wherein I is an identity matrix;
obtained posterior estimate AndAnd constructing a subgraph together with the prepositive mechanical laser radar data as the priori pose.
Further, in step 6, plane projection is performed on the front mechanical laser radar point cloud by using IMU data, and a subgraph is constructed, which specifically includes:
step 6-1, performing plane projection on the front mechanical laser radar point cloud by using a mapping formula:
wherein phi IMU is the pitch angle of the robot at the moment t calculated by IMU data, The x coordinate of the kth' point cloud data of the front laser radar of the robot at the moment t,Is thatThe mapped coordinates;
And 6-2, constructing a probability grid map by using point cloud data scanned by the front-end mechanical laser radar, dividing the point cloud data into different pixel areas, and setting a confidence coefficient P dp of the existence of the point cloud in each pixel area, wherein the confidence coefficient is continuously updated according to the input of the subsequent point cloud.
Further, in step 7, the motion increment between adjacent data frames of the pipeline inspection robot in the sliding window is estimated by using the sub-graph matching idea to tightly couple the priori pose and the local constraint of the sub-graph, and the maximum posterior estimation of the state of the pipeline inspection robot is solved by minimizing all factor residuals, which is specifically as follows:
Step 7-1, sub-graph matching is carried out by taking EKF output data as a priori pose, and the method specifically comprises the following steps:
wherein, the For a transformation matrix from a front mechanical laser radar coordinate system to a world coordinate system, N is the number of point clouds in one scanning period, a Com function is used for comparing point cloud data with probability grid map data according to pixel area division, if the point clouds fall in a map boundary area, the Com function value is 1, otherwise, the Com function value is 0, so that corresponding local constraint is obtained;
Step 7-2, tightly coupling the prior pose with the local constraint of the subgraph to estimate the motion increment of the robot between adjacent data frames in the sliding window, and minimizing all factor residuals to solve the maximum posterior estimation of the robot state The method comprises the following steps:
Wherein ρ is an evaluation function representing the degree of matching of the subgraphs, sub represents the set of constructed subgraphs, l and I represent subgraph sequence numbers, I k and I k+1 represent two adjacent frames of EKF data frames, AndRespectively representing two kinds of observed values,Representing an estimate of the state of the robot,The local constraints that represent the sub-graph,Representing a priori pose constraints of the EKF output.
On the other hand, a complex pipeline environment multi-source heterogeneous information fusion positioning system based on sub-graph matching is provided, and the system comprises:
the first module is used for establishing a pipeline inspection robot system dynamics model;
the second module is used for placing the pipeline inspection robot in a complex pipeline environment to collect heterogeneous sensor data, wherein the sensor comprises an encoder, an Inertial Measurement Unit (IMU), a side solid-state laser radar and a front mechanical laser radar;
the third module is used for preprocessing the collected heterogeneous sensor data and acquiring measurement information of the pipeline inspection robot and the surrounding environment thereof;
the fourth module is used for constructing a motion system equation of the pipeline inspection robot according to the encoder data and initializing the pose state and covariance of the pipeline inspection robot;
The fifth module is used for constructing and updating a measurement equation according to the IMU and the side solid-state laser radar data, calculating Kalman gain based on an Extended Kalman Filter (EKF) method and iteratively updating to obtain priori pose estimation of the pipeline inspection robot;
the sixth module is used for carrying out plane projection on the front mechanical laser radar point cloud by using the IMU data and constructing a subgraph;
and a seventh module, configured to use the sub-graph matching concept to tightly couple the priori pose and the local constraint of the sub-graph to estimate the motion increment between adjacent data frames of the pipeline inspection robot in the sliding window, and minimize all factor residuals to solve the maximum posterior estimation of the state of the pipeline inspection robot.
Compared with the prior art, the invention has the remarkable advantages that:
1) The invention relates to an autonomous real-time positioning method, which does not need to arrange auxiliary positioning equipment such as RFID tags in a pipeline in advance and does not need to obtain a priori map of the pipeline.
2) The invention utilizes the sub-graph matching concept, avoids the defect that the conventional positioning method is easy to fall into a local optimal solution, greatly improves the calculation efficiency of point cloud matching by taking the EKF output result as the priori pose, and remarkably reduces the running time and resource consumption for solving the global optimal pose.
3) The invention can obtain good effect in complex narrow pipeline environment, is beneficial to eliminating self-positioning accumulated errors of the pipeline inspection robot in complex pipeline environment, and improves the positioning precision of the pipeline robot.
The invention is described in further detail below with reference to the accompanying drawings.
Drawings
Fig. 1 is a hardware constitution diagram of a pipe inspection robot used in the present invention.
Fig. 2 is an overall block diagram of a positioning method according to the present invention.
Fig. 3 is a schematic diagram of a side solid state lidar point cloud image used in the present invention.
Fig. 4 is a diagram of the operation effect of the positioning method according to the present invention in several pipeline environments, where (a) to (c) in fig. 4 are a diagram of the operation effect in a straight pipeline environment, a diagram of the operation effect in a 90 ° bend pipeline environment, and a diagram of the operation effect in a U-bend pipeline environment, respectively.
Detailed Description
The present application will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present application more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the application.
It should be noted that, if directional indications (such as up, down, left, right, front, and rear are referred to in the embodiments of the present invention), the directional indications are merely used to explain the relative positional relationship, movement conditions, and the like between the components in a specific posture (as shown in the drawings), and if the specific posture is changed, the directional indications are correspondingly changed.
In addition, if there is a description of "first", "second", etc. in the embodiments of the present invention, the description of "first", "second", etc. is for descriptive purposes only and is not to be construed as indicating or implying a relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include at least one such feature. In addition, the technical solutions of the embodiments may be combined with each other, but it is necessary to base that the technical solutions can be realized by those skilled in the art, and when the technical solutions are contradictory or cannot be realized, the combination of the technical solutions should be considered to be absent and not within the scope of protection claimed in the present invention.
The invention aims to solve the problems of serious degradation and large accumulated error in similar scenes of the traditional positioning method, so that the methods of extended Kalman filtering, sub-graph matching and the like are comprehensively utilized to perform fusion processing on heterogeneous sensor data so as to solve the global optimal pose of a robot.
In one embodiment, in combination with fig. 1, there is provided a complex pipeline environment multi-source heterogeneous information fusion positioning method based on sub-graph matching, the method comprising the steps of:
step 1, establishing a pipeline inspection robot system dynamics model;
Step 2, placing a pipeline inspection robot in a complex pipeline environment to acquire heterogeneous sensor data, wherein the sensor comprises an encoder, an Inertial Measurement Unit (IMU), a side solid-state laser radar and a front mechanical laser radar;
Step 3, preprocessing the collected heterogeneous sensor information to obtain measurement information of the pipeline inspection robot and the surrounding environment thereof;
step 4, constructing a motion system equation of the pipeline inspection robot according to the encoder data, and initializing the pose state and covariance of the pipeline inspection robot;
step 5, constructing and updating a measurement equation according to the IMU and the side solid-state laser radar data, calculating Kalman gain based on an Extended Kalman Filtering (EKF) method, and iteratively updating to obtain prior pose estimation of the pipeline inspection robot;
step 6, carrying out plane projection on the front-end mechanical laser radar point cloud by using IMU data and constructing a subgraph (Subgraph);
And 7, estimating the motion increment of the robot between adjacent data frames in the sliding window by using the concept of sub-graph Matching (Subgraph-Matching) to tightly couple the priori pose and the local constraint of the sub-graph, and minimizing all factor residuals to solve the maximum posterior estimation of the state of the robot.
The described pipeline inspection robot is of differential drive type, and is provided with a wheel type encoder, an inertial measurement unit, a side solid-state laser radar and a front-mounted mechanical laser radar, and the integral hardware composition structural diagram of the pipeline inspection robot is shown in figure 1.
Further, in one embodiment, the system dynamics model of the pipeline inspection robot is built in the step 1, specifically:
Wherein v x,vy is the linear velocity of the motion along the x and y directions under the coordinate system of the pipeline inspection robot, ω is the angular velocity of the rotation of the coordinate system of the pipeline inspection robot relative to the global coordinate system, l is the track of the robot, Δψ is the variation of the yaw angle of the pipeline inspection robot at adjacent moments, ψ is the yaw angle of the pipeline inspection robot, and v l,vr is the linear velocity of the operation of the left and right wheels of the pipeline inspection robot.
Further, in one embodiment, the collecting heterogeneous sensor data in step 2 includes collecting encoder data of left and right wheels at time tTriaxial accelerometer data a x,ay,az, triaxial gyroscope data g x,gy,gz, triaxial magnetometer data m x,my,mz of IMU, point cloud data of side solid-state laser radarFront-mounted mechanical laser radar data
Further, in one embodiment, the preprocessing of the collected heterogeneous sensor data in step 3 to obtain pose information of the pipeline inspection robot specifically includes:
Step 3-1, respectively calculating left and right wheel linear speeds v l,vr of the pipeline inspection robot according to encoder data, wherein the method specifically comprises the following steps:
wherein M is the count of the encoder when the wheel of the pipeline inspection robot rotates for a complete circle, Left and right wheel encoder data at time t respectively,The data of left and right wheel encoders at the time t+1 are respectively obtained, d is the diameter of a wheel of the pipeline inspection robot, deltat is the time difference of two samples, and v x,vy and omega can be calculated based on the speeds of the left and right wheels;
Further, the pose change amounts Δx odom,Δyodom and Δψ odom of the pipe inspection robot within Δt calculated from the encoder data are represented specifically as follows:
Accumulating the delta x odom,Δyodom and the delta phi odom to obtain the coded odometer pose of the robot at each moment;
Step 3-2, filtering the IMU original data, namely obtaining corresponding quaternion data by fusing data of a gyroscope, an accelerometer and a magnetometer through a six-axis IMU complementary filtering method, wherein the method specifically comprises the following steps of:
wherein, the Representing the change of the IMU coordinate system relative to the world coordinate system, q 0,q1,q2,q3 represents a quaternion used for representing the rotation angle of the robot in the three-dimensional space;
Further, the quaternion calculated by the IMU data also obtains corresponding triaxial Euler angles, namely a roll angle theta IMU, a pitch angle phi IMU and a yaw angle phi IMU;
in step 3-3, the laser emitted by the side solid-state laser radar is in a straight line, so that in the pipeline environment with space limitation, the formed laser point cloud is generally shown in fig. 3.
Performing least square fitting on point cloud data between minus 20 degrees and plus 20 degrees of the side solid-state laser radar to obtain the distance between the side laser radar and the pipeline wall at the moment t
Further, the coordinate offset Δy lidar in the y-axis direction of the robot between adjacent moments is obtained, specifically:
Wherein I bool is a sign constant, +1 for the right laser radar I bool and-1 for the left laser radar I bool.
Further, in one embodiment, step 4 of constructing a motion system equation of the pipe inspection robot according to the encoder data, and initializing the pose state and covariance of the pipe inspection robot specifically includes:
Defining a state quantity x= [ x y psi ] T, and constructing a system equation f by using a measured value of an encoder to perform prior estimation on the pose of the robot, wherein the control quantity u= [ vω ] T is specifically as follows:
wherein, the For an a priori estimate of the time instant k,For the best posterior estimation at the time of k-1, [ v k ωk]T ] is the control quantity at the time of k, v k,ωk is the linear speed and the angular speed of the motion of the pipeline robot at the time of k, w k is the process noise of the system model at the time of k, and the Gaussian distribution P (w): N (0, Q) is satisfied, wherein Q is the covariance matrix of Gaussian noise.
Further, in one embodiment, in step 5, a measurement equation is constructed and updated according to IMU and side solid-state laser radar data, and a kalman gain is calculated and iteratively updated based on an Extended Kalman Filter (EKF) method, so as to obtain a priori pose estimation of the pipeline inspection robot, which specifically includes:
step 5-1, respectively calculating covariance matrixes of priori errors of IMU at k moment and side solid-state laser radar AndThe method comprises the following steps:
wherein, the AndRepresenting the error covariance matrix of the IMU and the solid-state lidar at time k-1, respectively, A k representing the jacobian matrix of the partial derivative of the system equation f with respect to x, and W k representing the jacobian matrix of the partial derivative of the system equation f with respect to W, specifically:
Step 5-2, respectively calculating the Kalman gains corresponding to the IMU at the k moment and the side solid-state laser radar AndThe method comprises the following steps:
Wherein H represents a jacobian matrix of a partial derivative of the sensor observation function H with respect to x, V represents a jacobian matrix of a partial derivative of the sensor observation function H with respect to V, H 'and V' are transposes of H and V, respectively, σ represents measurement noise, a Gaussian distribution, P (σ): (0, R), R is a measurement noise covariance matrix, and the variables related to H and V described above are expressed as:
wherein, the AndThe observed functions are respectively corresponding to the IMU and the solid-state laser radar, and the observed values are respectivelyAndThe corresponding predicted values are respectively
Wherein, the Representing the y-axis offset of the robot relative to the initial state obtained by the side lidar, step 5-3, calculating a posterior estimate using the obtained kalman gainAnd
Step 5-4, updating the error covariance matrix of the IMU and the solid-state laser radarAndThe method comprises the following steps:
Wherein I is an identity matrix;
obtained posterior estimate AndAnd constructing a subgraph together with the prepositive mechanical laser radar data as the priori pose.
Further, in one embodiment, in step 6, the plane projection is performed on the front mechanical laser radar point cloud by using IMU data and a subgraph is constructed, which specifically is:
step 6-1, performing plane projection on the front mechanical laser radar point cloud by using a mapping formula:
wherein phi IMU is the pitch angle of the robot at the moment t calculated by IMU data, The x coordinate of the kth' point cloud data of the front laser radar of the robot at the moment t,Is thatThe mapped coordinates;
And 6-2, constructing a probability grid map by using point cloud data scanned by the front-end mechanical laser radar, dividing the point cloud data into different pixel areas, and setting a confidence coefficient P dp of the existence of the point cloud in each pixel area, wherein the confidence coefficient is continuously updated according to the input of the subsequent point cloud.
Further, in one embodiment, in conjunction with fig. 2, step 7 uses the sub-graph matching concept to tightly couple the prior pose with the local constraint of the sub-graph to estimate the motion increment between the adjacent data frames of the pipeline inspection robot in the sliding window, and minimizes the residual error of all factors to solve the maximum posterior estimation of the state of the pipeline inspection robot, which is specifically as follows:
Step 7-1, sub-graph matching is carried out by taking EKF output data as a priori pose, and the method specifically comprises the following steps:
wherein, the For a transformation matrix from a front mechanical laser radar coordinate system to a world coordinate system, N is the number of point clouds in one scanning period, a Com function is used for comparing point cloud data with probability grid map data according to pixel area division, if the point clouds fall in a map boundary area, the Com function value is 1, otherwise, the Com function value is 0, so that corresponding local constraint is obtained;
Step 7-2, tightly coupling the prior pose with the local constraint of the subgraph to estimate the motion increment of the robot between adjacent data frames in the sliding window, and minimizing all factor residuals to solve the maximum posterior estimation of the robot state The method comprises the following steps:
Wherein the method comprises the steps of The local constraints that represent the sub-graph,Representing a priori pose constraints of the EKF output.
Fig. 4 shows positioning experiment results under several typical pipeline environments, the positioning system provided by the invention performs five groups of repeated experiments under the straight pipeline, the circular arc pipeline and the broken line pipeline respectively, positioning tracks of each experiment are marked with different colors, and the calculation result of the positioning method provided by the invention is correspondingly compared with the actual motion track of the robot. From experimental results, it can be considered that the positioning method provided by the invention obtains centimeter-level positioning accuracy in several pipeline environments.
In one embodiment, a complex pipeline environment multi-source heterogeneous information fusion positioning system based on sub-graph matching is provided, the system comprising:
the first module is used for establishing a pipeline inspection robot system dynamics model;
the second module is used for placing the pipeline inspection robot in a complex pipeline environment to collect heterogeneous sensor data, wherein the sensor comprises an encoder, an Inertial Measurement Unit (IMU), a side solid-state laser radar and a front mechanical laser radar;
the third module is used for preprocessing the collected heterogeneous sensor data and acquiring measurement information of the pipeline inspection robot and the surrounding environment thereof;
the fourth module is used for constructing a motion system equation of the pipeline inspection robot according to the encoder data and initializing the pose state and covariance of the pipeline inspection robot;
The fifth module is used for constructing and updating a measurement equation according to the IMU and the side solid-state laser radar data, calculating Kalman gain based on an Extended Kalman Filter (EKF) method and iteratively updating to obtain priori pose estimation of the pipeline inspection robot;
the sixth module is used for carrying out plane projection on the front mechanical laser radar point cloud by using the IMU data and constructing a subgraph;
and a seventh module, configured to use the sub-graph matching concept to tightly couple the priori pose and the local constraint of the sub-graph to estimate the motion increment between adjacent data frames of the pipeline inspection robot in the sliding window, and minimize all factor residuals to solve the maximum posterior estimation of the state of the pipeline inspection robot.
For specific limitation of the complex pipeline environment multi-source heterogeneous information fusion positioning system based on sub-graph matching, reference may be made to the limitation of the complex pipeline environment multi-source heterogeneous information fusion positioning method based on sub-graph matching hereinabove, and the description thereof is omitted here. All or part of each module in the complex pipeline environment multi-source heterogeneous information fusion positioning system based on sub-graph matching can be realized by software, hardware and combination thereof. The above modules may be embedded in hardware or may be independent of a processor in the computer device, or may be stored in software in a memory in the computer device, so that the processor may call and execute operations corresponding to the above modules.
In one embodiment, a computer device is provided that includes a memory, a processor, and a computer program stored on the memory and executable on the processor, the processor implementing when the computer program:
step 1, establishing a pipeline inspection robot system dynamics model;
Step 2, placing the pipeline inspection robot in a complex pipeline environment to acquire heterogeneous sensor data, wherein the sensor comprises an encoder, an Inertial Measurement Unit (IMU), a side solid-state laser radar and a front mechanical laser radar;
Step 3, preprocessing the collected heterogeneous sensor data to obtain measurement information of the pipeline inspection robot and the surrounding environment thereof;
step 4, constructing a motion system equation of the pipeline inspection robot according to the encoder data, and initializing the pose state and covariance of the pipeline inspection robot;
Step 5, constructing and updating a measurement equation according to the IMU and the side solid-state laser radar data, calculating Kalman gain based on an Extended Kalman Filter (EKF) method, and iteratively updating to obtain priori pose estimation of the pipeline inspection robot;
Step 6, carrying out plane projection on the front mechanical laser radar point cloud by using IMU data and constructing a subgraph;
and 7, estimating the motion increment between adjacent data frames of the pipeline inspection robot in the sliding window by using the sub-graph matching thought to tightly couple the priori pose and the local constraint of the sub-graph, and minimizing all factor residuals to solve the maximum posterior estimation of the state of the pipeline inspection robot.
For specific limitation of each step, reference may be made to the limitation of the multi-source heterogeneous information fusion positioning method of the complex pipeline environment based on sub-graph matching, which is not described herein.
The invention realizes the accurate and rapid real-time positioning of the pipeline inspection robot in the complex narrow pipeline environment, effectively reduces the problems of accumulated error and degradation generated by the traditional positioning method, and achieves the centimeter-level positioning accuracy.
The foregoing has outlined and described the basic principles, features, and advantages of the present invention. It will be understood by those skilled in the art that the foregoing embodiments are not intended to limit the invention, and the above embodiments and descriptions are meant to be illustrative only of the principles of the invention, and that various modifications, equivalent substitutions, improvements, etc. may be made within the spirit and scope of the invention without departing from the spirit and scope of the invention.
Claims (9)
1. The complex pipeline environment multi-source heterogeneous information fusion positioning method based on sub-graph matching is characterized by comprising the following steps of:
step 1, establishing a pipeline inspection robot system dynamics model;
step 2, placing the pipeline inspection robot in a complex pipeline environment to acquire heterogeneous sensor data, wherein the sensor comprises an encoder, an IMU, a side solid-state laser radar and a front mechanical laser radar;
Step 3, preprocessing the collected heterogeneous sensor data to obtain measurement information of the pipeline inspection robot and the surrounding environment thereof;
step 4, constructing a motion system equation of the pipeline inspection robot according to the encoder data, and initializing the pose state and covariance of the pipeline inspection robot;
Step 5, constructing and updating a measurement equation according to the IMU and the side solid-state laser radar data, calculating Kalman gain based on EKF and iteratively updating to obtain prior pose estimation of the pipeline inspection robot;
and 6, carrying out plane projection on the front-end mechanical laser radar point cloud by using IMU data and constructing a subgraph, wherein the method specifically comprises the following steps:
step 6-1, performing plane projection on the front mechanical laser radar point cloud by using a mapping formula:
wherein phi IMU is the pitch angle of the robot at the moment t calculated by IMU data, The x coordinate of the kth' point cloud data of the front laser radar of the robot at the moment t,Is thatThe mapped coordinates;
Step 6-2, constructing a probability grid map by using point cloud data scanned by a front-end mechanical laser radar, dividing the point cloud data into different pixel areas, and setting a confidence coefficient P dp of the existence of the point cloud in each pixel area, wherein the confidence coefficient is continuously updated according to the input of the subsequent point cloud;
And 7, estimating the motion increment between adjacent data frames of the pipeline inspection robot in the sliding window by using sub-graph matching tightly coupled priori pose and sub-graph local constraint, and minimizing all factor residuals to solve the maximum posterior estimation of the state of the pipeline inspection robot.
2. The complex pipeline environment multi-source heterogeneous information fusion positioning method based on sub-graph matching according to claim 1, wherein the system dynamics model of the pipeline inspection robot is established in the step 1, specifically:
Wherein v x,vy is the linear velocity of the motion along the x and y directions under the coordinate system of the pipeline inspection robot, ω is the angular velocity of the rotation of the coordinate system of the pipeline inspection robot relative to the global coordinate system, l is the track of the robot, Δψ is the variation of the yaw angle of the pipeline inspection robot at adjacent moments, ψ is the yaw angle of the pipeline inspection robot, and v l,vr is the linear velocity of the operation of the left and right wheels of the pipeline inspection robot.
3. The complex pipeline environment multi-source heterogeneous information fusion positioning method based on sub-graph matching according to claim 1 or 2, wherein the heterogeneous sensor data acquisition in step 2 comprises the steps of acquiring left and right wheel encoder data at t timeTriaxial accelerometer data a x,ay,az, triaxial gyroscope data g x,gy,gz, triaxial magnetometer data m x,my,mz of IMU, point cloud data of side solid-state laser radarFront-mounted mechanical laser radar data
4. The complex pipeline environment multi-source heterogeneous information fusion positioning method based on sub-graph matching according to claim 3, wherein the preprocessing of the collected heterogeneous sensor data in step 3 is performed to obtain the measurement information of the pipeline inspection robot and the surrounding environment thereof, and the method specifically comprises the following steps:
Step 3-1, respectively calculating the linear speeds v l,vr of the left wheel and the right wheel of the pipeline inspection robot according to encoder data, wherein the linear speeds v l,vr are specifically as follows:
wherein M is the count of the encoder when the wheel of the pipeline inspection robot rotates for a complete circle, Left and right wheel encoder data at time t respectively,The data of left and right wheel encoders at the time t+1 are respectively obtained, d is the diameter of a wheel of the pipeline inspection robot, and deltat is the time difference of two samples;
Further, the pose change amounts Δx odom,Δyodom and Δψ odom of the pipe inspection robot within Δt calculated from the encoder data are represented specifically as follows:
Accumulating the delta x odom,Δyodom and the delta phi odom to obtain the coded odometer pose of the robot at each moment;
step 3-2, filtering the IMU original data, namely obtaining corresponding quaternion data by fusing gyroscope and accelerometer data through a six-axis IMU complementary filtering method, wherein the method specifically comprises the following steps:
W Iq＝[q0 q1 q2 q3]T
Wherein W I q represents the change of the IMU coordinate system relative to the world coordinate system, and q 0,q1,q2,q3 represents a quaternion used for representing the rotation angle of the robot in the three-dimensional space;
Further, the quaternion calculated by the IMU data also obtains corresponding triaxial Euler angles, namely a roll angle theta IMU, a pitch angle phi IMU and a yaw angle phi IMU;
step 3-3, performing least square fitting on point cloud data between-20 degrees and +20 degrees of the side surface solid-state laser radar to obtain the distance between the side surface laser radar and the pipeline wall at the moment t
Further, the coordinate offset Δy lidar in the y-axis direction of the robot between adjacent moments is obtained, specifically:
Wherein I bool is a sign constant, +1 for the right laser radar I bool and-1 for the left laser radar I bool.
5. The complex pipeline environment multi-source heterogeneous information fusion positioning method based on sub-graph matching according to claim 4, wherein in step 4, a motion system equation of a pipeline inspection robot is constructed according to encoder data, and the initialization of the pose state and covariance of the pipeline inspection robot specifically comprises the following steps:
Defining a state quantity x= [ x y psi ] T, and constructing a system equation f by using a measured value of an encoder to perform prior estimation on the pose of the robot, wherein the control quantity u= [ vω ] T is specifically as follows:
wherein, the For an a priori estimate of the time instant k,For the best posterior estimation at the moment k-1, v k-1ωk-1]T is the control quantity at the moment k-1, v k-1,ωk-1 is the linear speed and the angular speed of the motion of the pipeline robot at the moment k-1, w k-1 is the process noise of the system model at the moment k-1, and the Gaussian distribution P (w) to N (0, Q) is satisfied, wherein Q is the covariance matrix of the Gaussian noise.
6. The complex pipeline environment multi-source heterogeneous information fusion positioning method based on sub-graph matching according to claim 5, wherein in step 5, a measurement equation is constructed and updated according to IMU and side solid-state laser radar data, kalman gain is calculated and iteratively updated based on an Extended Kalman Filter (EKF) method, and a priori pose estimation of a pipeline inspection robot is obtained, which specifically comprises:
step 5-1, respectively calculating covariance matrixes of priori errors of IMU at k moment and side solid-state laser radar AndThe method comprises the following steps:
wherein, the AndRepresenting the error covariance matrix of the IMU and the solid-state lidar at time k-1, respectively, A k representing the jacobian matrix of the partial derivative of the system equation f with respect to x, and W k representing the jacobian matrix of the partial derivative of the system equation f with respect to W, specifically:
Step 5-2, respectively calculating the Kalman gains corresponding to the IMU at the k moment and the side solid-state laser radar AndThe method comprises the following steps:
Wherein H represents a jacobian matrix of a partial derivative of the sensor observation function H with respect to x, V represents a jacobian matrix of a partial derivative of the sensor observation function H with respect to V, H 'and V' are transposes of H and V, respectively, σ represents measurement noise, gaussian distribution is satisfied, P (σ) - (0, R) is a measurement noise covariance matrix, and the variables related to H and V are represented as:
wherein, the AndThe observed functions are respectively corresponding to the IMU and the solid-state laser radar, and the observed values are respectivelyAndThe corresponding predicted values are respectively
Wherein, the Representing the y-axis offset of the robot relative to the initial state obtained by the side lidar;
Step 5-3, calculating a posterior estimate using the obtained Kalman gain And
Step 5-4, updating the error covariance matrix of the IMU and the solid-state laser radarAndThe method comprises the following steps:
Wherein I is an identity matrix;
obtained posterior estimate AndAnd constructing a subgraph together with the prepositive mechanical laser radar data as the priori pose.
7. The complex pipeline environment multi-source heterogeneous information fusion positioning method based on sub-graph matching according to claim 6, wherein the step 7 uses sub-graph matching to tightly couple the priori pose and the local constraint of the sub-graph to estimate the motion increment between adjacent data frames of the pipeline inspection robot in the sliding window, and minimizes all factor residuals to solve the maximum posterior estimation of the state of the pipeline inspection robot, specifically:
Step 7-1, sub-graph matching is carried out by taking EKF output data as a priori pose, and the method specifically comprises the following steps:
wherein, the For a transformation matrix from a front mechanical laser radar coordinate system to a world coordinate system, N is the number of point clouds in one scanning period, a Com function is used for comparing point cloud data with probability grid map data according to pixel area division, if the point clouds fall in a map boundary area, the Com function value is 1, otherwise, the Com function value is 0, so that corresponding local constraint is obtained;
Step 7-2, tightly coupling the prior pose with the local constraint of the subgraph to estimate the motion increment of the robot between adjacent data frames in the sliding window, and minimizing all factor residuals to solve the maximum posterior estimation of the robot state The method comprises the following steps:
Wherein ρ is an evaluation function representing the degree of matching of the subgraphs, sub represents the set of constructed subgraphs, l and I represent subgraph sequence numbers, I k and I k+1 represent two adjacent frames of EKF data frames, AndRespectively representing two kinds of observed values,Representing an estimate of the state of the robot,The local constraints that represent the sub-graph,Representing a priori pose constraints of the EKF output.
8. A complex pipeline environment multi-source heterogeneous information fusion positioning system based on sub-graph matching according to the method of any one of claims 1 to 7, characterized in that the system comprises:
the first module is used for establishing a pipeline inspection robot system dynamics model;
the second module is used for placing the pipeline inspection robot in a complex pipeline environment to collect heterogeneous sensor data, wherein the sensor comprises an encoder, an Inertial Measurement Unit (IMU), a side solid-state laser radar and a front mechanical laser radar;
the third module is used for preprocessing the collected heterogeneous sensor data and acquiring measurement information of the pipeline inspection robot and the surrounding environment thereof;
the fourth module is used for constructing a motion system equation of the pipeline inspection robot according to the encoder data and initializing the pose state and covariance of the pipeline inspection robot;
The fifth module is used for constructing and updating a measurement equation according to the IMU and the side solid-state laser radar data, calculating Kalman gain based on an Extended Kalman Filter (EKF) method and iteratively updating to obtain priori pose estimation of the pipeline inspection robot;
the sixth module is used for carrying out plane projection on the front mechanical laser radar point cloud by using the IMU data and constructing a subgraph;
and a seventh module, configured to use the sub-graph matching concept to tightly couple the priori pose and the local constraint of the sub-graph to estimate the motion increment between adjacent data frames of the pipeline inspection robot in the sliding window, and minimize all factor residuals to solve the maximum posterior estimation of the state of the pipeline inspection robot.
9. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the method of any of claims 1 to 7 when executing the computer program.
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