Intelligent autonomous navigation system based on laser radar and IMU fusion

Abstract

The invention discloses an intelligent autonomous navigation system based on laser radar and IMU fusion, and the system obtains environment three-dimensional point cloud data through the laser radar, and collects the acceleration and attitude angular velocity information of a robot in real time through the IMU; fusing the two types of data by adopting an extended Kalman filtering algorithm to realize high-precision positioning and attitude estimation; an environmental map is constructed in real time in combination with an SLAM technology, and global path planning and local dynamic obstacle avoidance are completed based on an A * algorithm and a dynamic window method; meanwhile, an anti-falling control module is integrated, dangerous areas such as edges and grooves are recognized by analyzing point cloud and attitude information, and operation safety is guaranteed. According to the method, the problems of insufficient positioning precision and poor dynamic obstacle avoidance capability of a single sensor in a complex industrial scene are effectively solved, and the autonomy, the precision and the safety of the robot in tasks such as polling and the like are remarkably improved.

Description

Technical Field

[0001] This invention belongs to the field of intelligent autonomous navigation and non-destructive testing technology, and in particular relates to an intelligent autonomous navigation system based on the fusion of lidar and IMU. Background Technology

[0002] In complex environments such as large power plants and industrial facilities, where equipment is densely distributed and space is limited, robots face higher demands on the accuracy of autonomous localization and path planning when performing inspection, testing, and maintenance tasks. Conventional navigation systems based on a single sensor are prone to problems such as large positioning errors, data loss, and untimely obstacle avoidance in dynamic environments or complex scenarios such as edges and ditches, leading to interruptions in inspection tasks or even safety hazards.

[0003] To address the aforementioned issues, multi-sensor fusion technology has been increasingly adopted in this field. LiDAR provides high-precision point cloud data and can construct real-time 3D maps of the environment; however, point cloud information is prone to errors during rapid movement or in the presence of occlusion. Inertial Measurement Units (IMUs), on the other hand, possess attitude and acceleration sensing capabilities, compensating for the shortcomings of LiDAR in short-term dynamic responses. However, relying solely on an IMU can easily lead to positioning drift due to accumulated errors. Therefore, how to complement the advantages of both technologies to improve the accuracy, robustness, and safety of the overall navigation system in complex environments has become a pressing technical challenge.

[0004] Traditional lidar suffers from poor adaptability to dynamic environments and rapid movement, making it prone to data loss or errors in complex environments, especially in complex power plant environments, which can lead to a decrease in positioning accuracy. While IMUs have good dynamic response capabilities, they are prone to accumulating errors when used alone, resulting in long-term positioning drift. Therefore, the limitations of existing single-sensor technologies cannot meet the requirements for high-precision autonomous navigation in complex environments.

[0005] Existing navigation systems cannot effectively integrate the global stability of lidar with the short-term dynamic response of IMU, resulting in low real-time positioning and path planning accuracy in fast-moving or complex environments, making it difficult to meet the high-precision requirements of power plant inspection, and lacking the ability to process environmental data that changes temporarily.

[0006] In complex industrial environments, robots need to navigate with high precision in complex terrain and confined spaces. Existing autonomous navigation systems may suffer from insufficient stability in complex environments due to inaccurate path planning algorithms and untimely dynamic responses, especially in areas with uneven ground or many obstacles, where the accuracy and stability of navigation control are difficult to guarantee. Therefore, it is necessary to design an intelligent autonomous navigation system based on the fusion of LiDAR and IMU to solve the above problems. Summary of the Invention

[0007] The technical problem this invention aims to solve is to provide an intelligent autonomous navigation system based on the fusion of lidar and IMU. By employing fusion algorithms such as extended Kalman filtering, the global stability of lidar is combined with the real-time dynamic response of the IMU to achieve high-precision mapping, positioning, and path planning. Furthermore, the system utilizes the A* algorithm and the Dynamic Window Method (DWA) to achieve autonomous obstacle avoidance and fall prevention control. This system can maintain high-precision positioning and stable operation in complex inspection environments, effectively improving operational efficiency and safety, and meeting the intelligent inspection needs of modern power plants and industrial scenarios.

[0008] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is as follows: An intelligent autonomous navigation system based on the fusion of lidar and IMU includes a lidar module for acquiring three-dimensional point cloud data of the robot's surrounding environment; An inertial measurement unit (IMU) is used to acquire the robot's acceleration and attitude angular velocity information in real time. The data processing and fusion module, connected to the lidar module and IMU, is used to receive and fuse 3D point cloud data with acceleration and attitude angular velocity information, and output high-precision positioning and attitude estimation results of the robot through the extended Kalman filter algorithm. The path planning module, connected to the data processing and fusion module, is used to plan the robot's motion path based on the high-precision positioning and attitude estimation results and the preset task objectives. The fall prevention control module, connected to the data processing and fusion module and the path planning module, is used to identify dangerous areas and generate avoidance or stop commands based on the fused environmental information and robot pose.

[0009] Preferably, the anti-fall control module analyzes the environmental three-dimensional point cloud data acquired by the lidar module in real time.

[0010] Preferably, the fall prevention control module identifies dangerous terrain features, including edges and ditches, and combines them with the robot's posture information fed back in real time by the IMU. When the robot's posture approaches a safety threshold or enters a dangerous area, it triggers an emergency stop or path replanning.

[0011] Preferably, the system further includes a Simultaneous Localization and Mapping (SLAM) module, which is integrated into the data processing and fusion module. This SLAM module is used to construct and update an environmental map in real time based on the fused LiDAR point cloud data and IMU data, while simultaneously enabling the robot to achieve precise positioning within the environmental map.

[0012] Preferably, the path planning module adopts a combination of the A* global path planning algorithm and the DWA local obstacle avoidance algorithm using the dynamic window method. The A* algorithm plans the initial path based on the global map, and the DWA algorithm dynamically adjusts the path based on real-time sensor data to avoid obstacles.

[0013] Furthermore, the lidar module is a RoboSense Helios 16 lidar, and the IMU is a Wittek JY901s.

[0014] Preferably, the system further includes a mode switching module for switching between manual control mode and automatic navigation mode according to instructions.

[0015] Preferably, a navigation method based on an intelligent autonomous navigation system fusion of lidar and IMU includes the following steps: S1 collects three-dimensional point cloud data of the robot's surrounding environment through the lidar module, and simultaneously collects the robot's acceleration and attitude angular velocity information in real time through the IMU. S2, through the data processing and fusion module, the extended Kalman filter algorithm is used to fuse the three-dimensional point cloud data with the acceleration and attitude angular velocity information to obtain the robot's high-precision positioning and attitude estimation results. S3, Based on the high-precision positioning and attitude estimation results, the environment map is constructed and updated in real time using simultaneous positioning and mapping technology; S4. Based on the environmental map, the robot's current position, and the preset task objective, the path planning module plans the global path, and the local obstacle avoidance algorithm is used to dynamically adjust the travel path in combination with real-time sensor data. S5, during the robot's movement, uses the anti-fall control module to analyze environmental point cloud data and robot posture information in real time, identify dangerous areas, and execute corresponding avoidance or stop control.

[0016] Preferably, in step S2, the fusion processing using the extended Kalman filter algorithm specifically includes: S201 uses the acceleration and angular velocity information provided by the IMU to predict the robot's pose at the next moment through a motion model; S202: The point cloud data acquired by the lidar module is matched with the current predicted pose through the observation model to calculate the residual between the observed and predicted values. S203, update the state estimate and covariance matrix of the extended Kalman filter based on the residual, and output the corrected high-precision positioning and attitude estimation results.

[0017] Preferably, in step S4, the A* algorithm is used for global path planning, and the dynamic window method is used for local path dynamic adjustment. The dynamic window method evaluates the trajectory corresponding to multiple sampled velocities of the robot in the velocity space and selects the optimal trajectory to simultaneously meet the requirements of target orientation, obstacle avoidance, and conformity to motion dynamics.

[0018] Preferably, step S5 specifically includes: By analyzing the terrain ahead using lidar point cloud data, we can identify areas with abrupt changes in height, such as edges or ditch areas. Real-time acquisition of robot roll and pitch angle information from IMU feedback; When the robot approaches a identified danger zone or its attitude angle exceeds a preset safety threshold, an emergency stop command is triggered or a path replanning request is sent to the path planning module to bypass the danger zone.

[0019] The beneficial effects of this invention are as follows: 1. By combining LiDAR and IMU (Inertial Measurement Unit) data fusion technology, high-precision positioning and navigation of robots in complex industrial environments were achieved. LiDAR provides high-resolution 3D point cloud data of the environment, while the IMU monitors the robot's acceleration and attitude information in real time. Accurate data fusion using the Extended Kalman Filter (EKF) algorithm significantly improves the robot's positioning stability and path planning accuracy in dynamic environments. Especially in rapidly moving or changing scenarios, it effectively avoids the impact of single sensor errors, ensuring smooth task execution. The deep fusion of the EKF algorithm and LiDAR point cloud effectively suppresses positioning drift errors during SLAM mapping. Especially during LiDAR scan failure intervals, IMU data can still maintain continuous pose estimation, enabling the system to maintain high-precision tilt angle safety threshold warnings in dangerous areas such as trenches and edges, significantly improving anti-interference capabilities and motion stability in complex industrial scenarios.

[0020] 2. Through SLAM (Simultaneous Localization and Mapping) technology, the robot can build a high-precision map of its surrounding environment in real time and achieve autonomous localization and path planning. Combined with the A* algorithm and the Dynamic Window Method (DWA), the system can dynamically adjust its path and avoid obstacles. Especially in complex environments, it ensures that the robot can handle complex operation scenarios such as dense obstacles or high-altitude operations with ease, greatly improving work efficiency and safety.

[0021] 3. The system's anti-fall and safety control mechanism effectively identifies potential hazardous areas such as edges and ditches through real-time fusion of LiDAR and IMU data. When approaching these areas, it automatically adjusts its path or stops moving forward, thereby preventing the robot from falling or colliding. This significantly improves safety during operation, especially in high-risk scenarios such as power plants, and effectively protects the safety of both the robot and personnel. Attached Figure Description

[0022] Figure 1 This is a flowchart illustrating an embodiment of the present invention; Figure 2 This is a schematic diagram of the laser radar obstacle avoidance and navigation principle in an embodiment of the present invention; Figure 3 This is a schematic diagram of the internal structure of the IMU in an embodiment of the present invention; Figure 4 This is a schematic diagram of the basic steps of the SLAM algorithm in an embodiment of the present invention; Figure 5 This is a schematic diagram of the SLAM mapping results in an embodiment of the present invention; Figure 6 This is a schematic diagram of EKF positioning in an embodiment of the present invention; Figure 7 This is a flowchart of the A* algorithm path planning in an embodiment of the present invention. Detailed Implementation

[0023] Example 1: like Figures 1-7 As shown, an intelligent autonomous navigation system based on the fusion of lidar and IMU includes a lidar module for acquiring three-dimensional point cloud data of the robot's surrounding environment. An inertial measurement unit (IMU) is used to acquire the robot's acceleration and attitude angular velocity information in real time. The data processing and fusion module, connected to the lidar module and IMU, is used to receive and fuse 3D point cloud data with acceleration and attitude angular velocity information, and output high-precision positioning and attitude estimation results of the robot through the extended Kalman filter algorithm. The path planning module, connected to the data processing and fusion module, is used to plan the robot's motion path based on the high-precision positioning and attitude estimation results and the preset task objectives. The fall prevention control module, connected to the data processing and fusion module and the path planning module, is used to identify dangerous areas and generate avoidance or stop commands based on the fused environmental information and robot pose.

[0024] Preferably, the anti-fall control module analyzes the environmental three-dimensional point cloud data acquired by the lidar module in real time.

[0025] Preferably, the fall prevention control module identifies dangerous terrain features, including edges and ditches, and combines them with the robot's posture information fed back in real time by the IMU. When the robot's posture approaches a safety threshold or enters a dangerous area, it triggers an emergency stop or path replanning.

[0026] Preferably, the system further includes a Simultaneous Localization and Mapping (SLAM) module, which is integrated into the data processing and fusion module. This SLAM module is used to construct and update an environmental map in real time based on the fused LiDAR point cloud data and IMU data, while simultaneously enabling the robot to achieve precise positioning within the environmental map.

[0027] Preferably, the path planning module adopts a combination of the A* global path planning algorithm and the DWA local obstacle avoidance algorithm using the dynamic window method. The A* algorithm plans the initial path based on the global map, and the DWA algorithm dynamically adjusts the path based on real-time sensor data to avoid obstacles.

[0028] Furthermore, the lidar module is a RoboSense Helios 16 lidar, and the IMU is a Wittek JY901s.

[0029] Preferably, the system further includes a mode switching module for switching between manual control mode and automatic navigation mode according to instructions.

[0030] Preferably, a navigation method based on an intelligent autonomous navigation system fusion of lidar and IMU includes the following steps: S1 collects three-dimensional point cloud data of the robot's surrounding environment through the lidar module, and simultaneously collects the robot's acceleration and attitude angular velocity information in real time through the IMU. S2, through the data processing and fusion module, the extended Kalman filter algorithm is used to fuse the three-dimensional point cloud data with the acceleration and attitude angular velocity information to obtain the robot's high-precision positioning and attitude estimation results. S3, Based on the high-precision positioning and attitude estimation results, the environment map is constructed and updated in real time using simultaneous positioning and mapping technology; S4. Based on the environmental map, the robot's current position, and the preset task objective, the path planning module plans the global path, and the local obstacle avoidance algorithm is used to dynamically adjust the travel path in combination with real-time sensor data. S5, during the robot's movement, uses the anti-fall control module to analyze environmental point cloud data and robot posture information in real time, identify dangerous areas, and execute corresponding avoidance or stop control.

[0031] Preferably, in step S2, the fusion processing using the extended Kalman filter algorithm specifically includes: S201 uses the acceleration and angular velocity information provided by the IMU to predict the robot's pose at the next moment through a motion model; S202: The point cloud data acquired by the lidar module is matched with the current predicted pose through the observation model to calculate the residual between the observed and predicted values. S203, update the state estimate and covariance matrix of the extended Kalman filter based on the residual, and output the corrected high-precision positioning and attitude estimation results.

[0032] Preferably, in step S4, the A* algorithm is used for global path planning, and the dynamic window method is used for local path dynamic adjustment. The dynamic window method evaluates the trajectory corresponding to multiple sampled velocities of the robot in the velocity space and selects the optimal trajectory to simultaneously meet the requirements of target orientation, obstacle avoidance, and conformity to motion dynamics.

[0033] Preferably, step S5 specifically includes: By analyzing the terrain ahead using lidar point cloud data, we can identify areas with abrupt changes in height, such as edges or ditch areas. Real-time acquisition of robot roll and pitch angle information from IMU feedback; When the robot approaches a identified danger zone or its attitude angle exceeds a preset safety threshold, an emergency stop command is triggered or a path replanning request is sent to the path planning module to bypass the danger zone.

[0034] Example 2: The intelligent autonomous navigation system of this invention primarily relies on data fusion from LiDAR and IMU (Inertial Measurement Unit), achieving precise positioning and navigation through an Extended Kalman Filter (EKF) algorithm. LiDAR provides a 3D map of the environment using high-resolution point cloud data, while the IMU tracks the robot's acceleration, attitude, and angular velocity in real time, compensating for potential errors in the LiDAR in dynamic environments. This data fusion effectively improves the robot's positioning accuracy and path planning capabilities in complex environments, ensuring stable operation during rapid movement or in complex terrain.

[0035] In this system, the robot utilizes SLAM (Simultaneous Localization and Mapping) technology, combined with LiDAR and IMU data, to construct a high-precision map of its surrounding environment in real time. The SLAM algorithm not only provides the robot with real-time self-localization information but also offers reliable data support for path planning. Relying on path planning methods such as the A* algorithm and Dynamic Window Method (DWA), the robot can flexibly cope with various complex obstacles, automatically calculating and adjusting the optimal path to avoid collisions or stalling.

[0036] To enhance robot safety in complex working environments, this system also incorporates a fall prevention system. Through the collaborative operation of LiDAR and IMU, the robot can monitor dangerous areas such as edges and ditches in real time. Upon detecting proximity to a dangerous area, the system dynamically adjusts its path or initiates an emergency stop to prevent falls or collisions, ensuring operational safety. Furthermore, the system's multi-sensor redundancy design ensures that the robot can continue to function normally even if a single sensor fails, significantly improving system stability and reliability.

[0037] The system also features both manual and automatic operating modes to meet different operational needs. In manual mode, the operator can control the robot's movement in real time via a remote control or control panel, ensuring precise operation in special environments or complex tasks. In automatic mode, the robot relies entirely on its autonomous navigation system to perform tasks, automatically planning paths, avoiding obstacles, adjusting speed, and using sensor feedback for real-time environmental perception and emergency response, greatly improving work efficiency.

[0038] Through the above implementation methods, the intelligent autonomous navigation system of the present invention, by combining lidar and IMU fusion technology with advanced path planning and anti-fall control algorithms, can complete efficient and safe inspection tasks in complex industrial environments such as power plants, meeting the high-precision operation requirements of modern industry for intelligent and automated operations.

Claims

1. An intelligent autonomous navigation system based on the fusion of lidar and IMU, characterized in that, Includes a lidar module, used to acquire 3D point cloud data of the robot's surrounding environment; An inertial measurement unit (IMU) is used to acquire the robot's acceleration and attitude angular velocity information in real time. The data processing and fusion module, connected to the lidar module and IMU, is used to receive and fuse 3D point cloud data with acceleration and attitude angular velocity information, and output high-precision positioning and attitude estimation results of the robot through the extended Kalman filter algorithm. The path planning module, connected to the data processing and fusion module, is used to plan the robot's motion path based on the high-precision positioning and attitude estimation results and the preset task objectives. The fall prevention control module, connected to the data processing and fusion module and the path planning module, is used to identify dangerous areas and generate avoidance or stop commands based on the fused environmental information and robot pose.

2. The intelligent autonomous navigation system based on the fusion of lidar and IMU according to claim 1, characterized in that, The anti-fall control module analyzes the environmental three-dimensional point cloud data acquired by the lidar module in real time.

3. The intelligent autonomous navigation system based on the fusion of lidar and IMU according to claim 1, characterized in that, The fall prevention control module identifies dangerous terrain features, including edges and ditches, and combines them with the robot's posture information fed back in real time by the IMU. When the robot's posture approaches a safety threshold or enters a dangerous area, it triggers an emergency stop or path replanning.

4. The intelligent autonomous navigation system based on the fusion of lidar and IMU according to claim 3, characterized in that, The system also includes a Simultaneous Localization and Mapping (SLAM) module, which is integrated into the data processing and fusion module. This SLAM module is used to build and update an environmental map in real time based on the fused LiDAR point cloud data and IMU data, while simultaneously enabling the robot to achieve precise positioning within the environmental map.

5. The intelligent autonomous navigation system based on the fusion of lidar and IMU according to claim 1, characterized in that, The path planning module adopts a combination of the A* global path planning algorithm and the DWA local obstacle avoidance algorithm using the dynamic window method. The A* algorithm plans the initial path based on the global map, and the DWA algorithm dynamically adjusts the path based on real-time sensor data to avoid obstacles.

6. The intelligent autonomous navigation system based on the fusion of lidar and IMU according to claim 5, characterized in that, The system also includes a mode switching module, which is used to switch between manual control mode and automatic navigation mode according to instructions.

7. A navigation method for an intelligent autonomous navigation system based on the fusion of lidar and IMU according to any one of claims 1-6, characterized in that, Includes the following steps: S1 collects three-dimensional point cloud data of the robot's surrounding environment through the lidar module, and simultaneously collects the robot's acceleration and attitude angular velocity information in real time through the IMU. S2, through the data processing and fusion module, the extended Kalman filter algorithm is used to fuse the three-dimensional point cloud data with the acceleration and attitude angular velocity information to obtain the robot's high-precision positioning and attitude estimation results. S3, Based on the high-precision positioning and attitude estimation results, the environment map is constructed and updated in real time using simultaneous positioning and mapping technology; S4. Based on the environmental map, the robot's current position, and the preset task objective, the path planning module plans the global path, and the local obstacle avoidance algorithm is used to dynamically adjust the travel path in combination with real-time sensor data. S5, during the robot's movement, uses the anti-fall control module to analyze environmental point cloud data and robot posture information in real time, identify dangerous areas, and execute corresponding avoidance or stop control.

8. The navigation method of an intelligent autonomous navigation system based on the fusion of lidar and IMU according to claim 7, characterized in that, In step S2, the fusion processing using the extended Kalman filter algorithm specifically includes: S201 uses the acceleration and angular velocity information provided by the IMU to predict the robot's pose at the next moment through a motion model; S202: The point cloud data acquired by the lidar module is matched with the current predicted pose through the observation model to calculate the residual between the observed and predicted values. S203, update the state estimate and covariance matrix of the extended Kalman filter based on the residual, and output the corrected high-precision positioning and attitude estimation results.

9. The navigation method of an intelligent autonomous navigation system based on the fusion of lidar and IMU according to claim 7, characterized in that, In step S4, the A* algorithm is used for global path planning, and the dynamic window method is used for local path dynamic adjustment. The dynamic window method evaluates the trajectory corresponding to multiple sampled velocities of the robot in the velocity space and selects the optimal trajectory to simultaneously meet the requirements of target orientation, obstacle avoidance and conformity to motion dynamics.

10. A navigation method for an intelligent autonomous navigation system based on the fusion of lidar and IMU according to claim 7, characterized in that, Step S5 specifically includes: By analyzing the terrain ahead using lidar point cloud data, we can identify areas with abrupt changes in height, such as edges or ditch areas. Real-time acquisition of robot roll and pitch angle information from IMU feedback; When the robot approaches a identified danger zone or its attitude angle exceeds a preset safety threshold, an emergency stop command is triggered or a path replanning request is sent to the path planning module to bypass the danger zone.

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