A High-Precision Localization Method for Driverless Buses Based on SLAM Maps

Abstract

The present invention discloses a high-precision positioning method for driverless buses based on SLAM maps, which relates to the field of driverless technologies and includes the following steps: obtaining lidar data, inertial measurement data, odometer data, and visual data of the driverless bus; parsing vehicle control instructions, simulating the motion trajectory of the driverless bus through a particle filter algorithm, and generating a pose hypothesis set; performing position matching between the lidar data and a pre-constructed SLAM map, and performing auxiliary positioning in low visibility scenarios to obtain a position matching result; constructing an initial static map and detecting dynamic obstacles to generate a dynamic and static fusion SLAM map; performing status monitoring and triggering a multi-level fault recovery strategy. This method significantly improves the positioning accuracy and system stability of driverless buses through timestamp alignment and Kalman filter dynamic fusion, adjustment of harsh environment parameters, and a multi-level fault recovery mechanism.

Description

Technical Field

[0001] The present invention relates to the field of driverless technology, and more specifically to a high-precision positioning method for driverless buses based on SLAM maps. Background Art

[0002] As an important part of intelligent transportation systems, driverless technology is gradually changing the face of urban public transportation. Especially in the field of driverless buses, by integrating advanced sensors, computing platforms, and control algorithms, it aims to achieve safe and efficient urban public transportation services. It needs to accurately obtain its own position in various complex environments for precise path planning and obstacle avoidance operations.

[0003] In the prior art, multi-source data fusion is a key link in achieving precise positioning of driverless buses. However, traditional methods often rely on simple superposition or asynchronous processing of sensor data, which leads to data accumulation error problems caused by time asynchrony, affecting the stability and real-time performance of the positioning system. And existing fusion algorithms fail to fully consider the noise differences between sensor data, resulting in inaccurate motion state estimation, limiting the safety and reliability of driverless buses.

[0004] Secondly, for special situations such as low visibility environments like haze and rainy days, existing positioning technologies mainly rely on a single lidar for environmental perception. However, in adverse weather conditions, the effective detection range and accuracy of lidar will be greatly affected. It also lacks effective auxiliary means to make up for the inaccurate positioning problem caused by the performance degradation of lidar, making it difficult for driverless buses to maintain normal operation under such conditions.

[0005] In addition, for the recognition and processing of dynamic obstacles, most current technologies use static map construction and simple obstacle classification methods, which are difficult to adapt to dynamic elements such as pedestrians and vehicles that frequently appear on urban roads. This kind of limitation not only reduces the efficiency of path planning but also may lead to traffic accidents due to misjudgment. At the same time, existing systems lack a flexible recovery mechanism when encountering faults, and an error may cause the entire system to fail.

[0006] Therefore, how to design a high-precision positioning method for driverless buses based on SLAM maps, which can effectively fuse multi-source data, improve the positioning accuracy in low visibility environments, and have the ability to intelligently process dynamic obstacles and quickly recover from faults, is an urgent problem to be solved by those skilled in the art. Summary of the Invention

[0007] In view of this, the present invention provides a high-precision positioning method for driverless buses based on SLAM maps, which provides more reliable and efficient positioning and navigation support for driverless buses through precise time synchronization and data fusion, an adaptive low visibility enhancement strategy, and a dynamic obstacle hierarchical modeling and multi-level fault tolerance mechanism, so as to meet the safe operation requirements in complex urban environments.

[0008] To achieve the above object, the present invention adopts the following technical solutions:

[0009] A high-precision positioning method for driverless buses based on SLAM maps, comprising the following steps:

[0010] S1. Obtain the lidar data, inertial measurement data, odometer data, and visual data of the driverless bus, perform timestamp synchronization and integrity verification, and fuse the inertial measurement data and the odometer data;

[0011] S2. Use the fused data to parse the vehicle control commands, simulate the motion trajectory of the driverless bus through the particle filter algorithm, and generate a pose hypothesis set;

[0012] S3. Based on the pose hypothesis set, perform position matching between the lidar data and the pre-constructed SLAM map, and perform auxiliary positioning in low visibility scenarios to obtain a position matching result;

[0013] S4. Through the position matching result, perform initial static map construction and dynamic obstacle detection to generate a dynamic and static fusion SLAM map;

[0014] S5. Perform state monitoring under the dynamic and static fusion SLAM map and trigger a multi-level fault recovery strategy.

[0015] Further, in the S1, the timestamp synchronization and integrity verification include:

[0016] Timestamp synchronization: Taking the timestamp of the lidar data as a reference, align the inertial measurement data and the odometer data to the same time series through the linear interpolation algorithm;

[0017] Integrity verification: Verify that the number of lidar data points per frame > 500, the absolute value of the acceleration of the inertial measurement data ≤ 30 ± m / s 2 , the absolute value of the odometer data ≤ 15 m / s; if the data is incomplete, the missing data is complemented by combining the time interpolation algorithm.

[0018] Further, in the S1, the Kalman filter algorithm is used to fuse the inertial measurement data and the odometer data;

[0019] The state prediction equation of the Kalman filter algorithm is expressed as:

[0020] x' k = A·x' k−1 + B·u k

[0021] where x' k represents the state prediction value at time k, and x' k−1 represents the state prediction value at time k−1, u k represents the control input vector, A represents the state transition matrix, and B represents the control input matrix;

[0022] The state update equation of the Kalman filter algorithm is expressed as:

[0023] x k = x' k + K·(z k - H·x' k )

[0024] where x k represents the state update value at time k, z k represents the state measurement value at time k, K represents the Kalman gain weight matrix, and H represents the observation matrix.

[0025] Furthermore, in S2, parsing the vehicle control instruction includes:

[0026] According to the speed v t and the steering angular velocity ω t , calculate the displacement increment Δx and the angle increment Δθ; and set the maximum vehicle speed to 15 m / s and the maximum steering angular velocity to ±0.5 rad / s. If the speed or the steering angular velocity exceeds the threshold, trigger the limit constraint.

[0027] Furthermore, in S2, the calculation equation for the movement of each particle in the particle filter algorithm is:

[0028] x i,t = x i,t-1 + Δx + N1(0, σ x )

[0029] θ i,t = θ i,t-1 + Δθ + N2(0, σ θ )

[0030] where x i,t represents the position coordinate of particle i at time t, x i,t-1 represents the position coordinate of particle i at time t - 1, N1 represents the position noise with a mean of 0 and a standard deviation of σ x , and θ i,tRepresents the angular coordinate of particle i at time t, θ i,t-1 Represents the angular coordinate of particle i at time t-1, and N2 represents angular noise with a mean of 0 and a standard deviation of σ θ of the angle.

[0031] Furthermore, in the S3, the position matching of the lidar data and the pre-constructed SLAM map includes: using the NDT algorithm to map the lidar data to the grid map and calculating the position matching probability of each particle :

[0032]

[0033] where z t represents the current observed point cloud, h(x i,t ) represents the predicted point cloud corresponding to the position coordinate of particle i at time t, ||·|| represents the Euclidean distance, exp(·) represents the exponential function, and σ represents the standard deviation of the matching error.

[0034] Furthermore, in the S3, the auxiliary positioning in low visibility scenarios includes:

[0035] Restrict the effective point cloud detection distance to 20 m, and adjust the lidar acquisition frequency to 15 Hz or 20 Hz in combination with the weather conditions;

[0036] Extract road markings and traffic signs by combining visual data to generate auxiliary positioning feature points and perform weighted fusion with the position matching results.

[0037] Furthermore, in the S4, the initial static map construction includes:

[0038] Project the initial point cloud onto a two-dimensional grid map with a resolution of 0.1 m, and mark obstacles as non-passable areas; if the map construction fails, reduce the resolution to 0.2 m and increase the number of point cloud acquisitions; if it still fails, prohibit the vehicle from starting and trigger an alarm.

[0039] Furthermore, in the S4, the dynamic obstacle detection includes:

[0040] Extract dynamic obstacles by point cloud difference and set speed thresholds for classification;

[0041] Use the YOLO algorithm to identify the types of obstacles in the visual data and dynamically assign weight priorities according to the distance;

[0042] In addition, raise the lower limit of the dynamic obstacle speed threshold to 0.5 m / s in rainy weather scenarios and correct the vehicle slip error through the inertial measurement unit.

[0043] Furthermore, in the S5, the multi-level fault recovery strategy includes:

[0044] When the average particle weight < 0.01, the sensor data loss lasts for ≥ 1 second, or the map update delay ≥ 500 ms, it is determined that the positioning fails; then reset the particle distribution to the state with the highest historical weight, and remove the latest dynamic obstacle marker and update to the previous frame map version;

[0045] If the map update fails, enable the block storage mechanism and only update the local map within 30 meters around.

[0046] As can be seen from the above technical solutions, compared with the prior art, the technical solutions of the present invention have the following beneficial effects:

[0047] 1. The method aligns multi-sensor data through a linear interpolation algorithm based on the lidar timestamp, and combines the Kalman filtering algorithm to fuse the inertial measurement unit and odometer data, significantly improving the data consistency and the stability of the motion state estimation. Compared with the asynchronous or simple superposition fusion method of sensor data in the prior art, this method effectively solves the cumulative error problem caused by the time asynchrony of multi-source data, and enhances the real-time performance and reliability of the positioning system.

[0048] 2. For harsh environments such as haze and rainy days, it proposes to limit the effective detection distance of the lidar, dynamically adjust the point cloud acquisition frequency, and fuse visual data to extract road markings and traffic signs as auxiliary positioning feature points. Through multi-modal data complementarity and parameter adaptive adjustment, it improves the environmental adaptability in low visibility scenarios.

[0049] 3. Combining point cloud difference, speed threshold classification and YOLO object detection algorithm, it realizes the accurate recognition of dynamic obstacles and the dynamic weighting of priorities; at the same time, a multi-level fault recovery strategy is designed, including hierarchical response measures such as particle weight reset, local map rollback, and emergency stop. Through the hierarchical modeling of dynamic obstacles and multi-level fault tolerance logic, it greatly improves the system safety and anti-interference ability in complex urban road scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only the embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained according to the provided drawings.

[0051] Figure 1 It is a flowchart of a high-precision positioning method for an autonomous bus based on a SLAM map provided by an embodiment of the present invention;

[0052] Figure 2This is the flowchart of the multi-level fault recovery strategy provided by the embodiments of the present invention. Detailed implementation manners

[0053] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0054] As Figure 1 shown, this embodiment provides a high-precision positioning method for driverless buses based on a SLAM map, including:

[0055] S1. Obtain the lidar data, inertial measurement data, odometer data, and visual data of the driverless bus, perform timestamp synchronization and integrity verification, and fuse the inertial measurement data and odometer data;

[0056] S2. Use the fused data to parse the vehicle control command, simulate the motion trajectory of the driverless bus through the particle filter algorithm, and generate a pose hypothesis set;

[0057] S3. Based on the pose hypothesis set, perform position matching between the lidar data and the pre-constructed SLAM map, and perform assisted positioning in low visibility scenarios to obtain a position matching result;

[0058] S4. Through the position matching result, perform initial static map construction and dynamic obstacle detection to generate a dynamic and static fusion SLAM map;

[0059] S5. Perform state monitoring under the dynamic and static fusion SLAM map and trigger a multi-level fault recovery strategy.

[0060] This method realizes multi-source data fusion through linear interpolation of the lidar timestamp benchmark combined with Kalman filtering, improving the stability and real-time performance of motion state estimation. For low visibility scenarios, the lidar detection parameters are adaptively adjusted and visual data is fused to enhance the positioning robustness. In the processing of dynamic obstacles, point cloud difference, speed threshold classification, and YOLO detection are used for accurate identification, and a multi-level fault recovery strategy is designed to improve the system safety and anti-interference ability.

[0061] The following further elaborates on each of the above steps in detail:

[0062] In this embodiment S1, the lidar data, inertial measurement data, odometer data, and visual data of the driverless bus are acquired, timestamp synchronization and integrity verification are performed, and the inertial measurement data and odometer data are fused.

[0063] Specifically, the specific acquisition methods of the above data are as follows:

[0064] Lidar data: The surrounding environment point cloud data is collected in real time by the lidar. Each frame of point cloud data contains three-dimensional coordinates and reflection intensity information. The sampling frequency is 10 times per second, the point cloud resolution is 0.1 meter, and the data format is pcd.

[0065] Inertial measurement data: The information of the vehicle's acceleration and angular velocity is collected by the inertial measurement unit IMU. The sampling frequency is 100 Hz, the data format is 6 dimensions, with a timestamp attached, and the format is json.

[0066] Odometer data: The speed of the vehicle (error accuracy: ±0.05) and the steering angular velocity (±0.01) are collected by the odometer. The collection frequency is 50 Hz.

[0067] Visual data: A high-definition camera is used to provide supplementary positioning data for the autonomous driving bus in complex scenarios such as haze environment, urban traffic main roads, and complex intersections.

[0068] When the lidar data is lost, the driverless bus will automatically switch to using the inertial measurement data and odometer data for compensation to maintain the basic navigation function of the vehicle. In addition, if the acceleration detected by the inertial measurement unit IMU exceeds the set threshold, or the speed change shown by the odometer is abnormal, the vehicle deceleration or pause operation will be triggered. This mechanism ensures that the vehicle can drive safely even when part of the sensor data is lost and effectively prevents risks caused by abnormal data.

[0069] Furthermore, the timestamp synchronization and integrity verification include:

[0070] Timestamp synchronization: Based on the timestamp of the lidar data, the inertial measurement data and odometer data are aligned to the same time series through the linear interpolation algorithm.

[0071] Integrity verification: Check the timestamp of each frame of data. The time interval of each frame of lidar point cloud ≤ 100 ms, the time interval of each frame of inertial measurement unit data ≤ 10 ms, and the time interval of each frame of odometer data ≤ 20 ms; and verify that the number of points in each frame of lidar data > 500, the absolute value of the acceleration of inertial measurement data ≤ 30 ± m / s 2 and the absolute value of odometer data ≤ 15 m / s; if the data is incomplete, the missing data is complemented by combining the time interpolation algorithm.

[0072] Furthermore, the Kalman filter algorithm is used to fuse the inertial measurement data and the odometer data;

[0073] The state prediction equation of the Kalman filter algorithm is expressed as:

[0074] x’ k =A·x’ k−1 +B·u k

[0075] where, x’ k represents the state prediction value at time k, x’ k−1 represents the state prediction value at time k−1, u k represents the control input vector, A represents the state transition matrix, and B represents the control input matrix;

[0076] The state update equation of the Kalman filter algorithm is expressed as:

[0077] x k =x’ k +K·(z k -H·x’ k )

[0078] where, x k represents the state update value at time k, z k represents the state measurement value at time k, K represents the Kalman gain weight matrix, and H represents the observation matrix.

[0079] Furthermore, if the fusion fails, the lidar point cloud is directly used to generate a temporary position. In addition, if the Kalman gain weight matrix K is unstable (i.e., the numerical value diverges) during the Kalman filter update process, the filter state is re-initialized.

[0080] This step solves the multi-sensor timing difference problem through the timestamp synchronization and data compensation mechanism; uses the Kalman filter to improve the accuracy and real-time performance of the motion state estimation; automatically switches to the backup data source when the sensor fails, and combines the dynamic threshold to trigger deceleration or pause operations, significantly enhancing the robustness, fault tolerance, and driving safety of the system.

[0081] In this embodiment S2, the fused data is used to parse the vehicle control instructions, and the particle filter algorithm is used to simulate the motion trajectory of the driverless bus to generate a pose hypothesis set;

[0082] Among them, parsing the vehicle control instructions includes:

[0083] According to the speed v t and the steering angular velocity ω t, calculate the displacement increment Δx and the angle increment Δθ; and set the maximum vehicle speed to 15 m / s and the maximum steering angular velocity to ±0.5 rad / s. If the speed or steering angular velocity exceeds the threshold, the amplitude limiting constraint is triggered.

[0084] Further, in S2, the calculation equation for the movement of each particle in the particle filter algorithm is:

[0085] x i,t =x i,t-1 +Δx+N1(0,σ x )

[0086] θ i,t =θ i,t-1 +Δθ+N2(0,σ θ )

[0087] Wherein, x i,t represents the position coordinate of particle i at time t, x i,t-1 represents the position coordinate of particle i at time t - 1, N1 represents the position noise with a mean of 0 and a standard deviation of σ x ; θ i,t represents the angle coordinate of particle i at time t, θ i,t-1 represents the angle coordinate of particle i at time t - 1, N2 represents the angle noise with a mean of 0 and a standard deviation of σ θ .

[0088] Further, the standard deviation σ x of the position noise is set to 0.05 meters, and the standard deviation σ θ of the angle noise is set to 0.01 radians.

[0089] In this step, based on the fused data, the vehicle control command is parsed, the motion trajectory is simulated through the particle filter algorithm, the position noise and angle noise models are introduced to enhance the prediction diversity, and the amplitude limiting constraint is combined to ensure driving safety. Among them, the particle filter algorithm effectively processes the non-linear motion model, and the optimization of the noise parameters improves the coverage and robustness of the hypothesis set; the dynamic constraint mechanism suppresses the speeding risk in real time, ensuring the accuracy of the trajectory prediction and the stability of the vehicle control, and providing a reliable pose hypothesis basis for subsequent positioning and decision-making.

[0090] In S3 of this embodiment, based on the pose hypothesis set, the lidar data is matched with the pre-constructed SLAM map in terms of position, and auxiliary positioning is performed in low visibility scenarios to obtain a position matching result;

[0091] Among them, the position matching of the lidar data with the pre-constructed SLAM map includes:

[0092] In a conventional environment, points within 30 - 50 meters in front of the vehicle are retained, and the NDT algorithm is used to map the lidar data to a grid map, and the position matching probability of each particle is calculated. :

[0093]

[0094] Among them, z t represents the current observed point cloud, h(x i,t ) represents the predicted point cloud corresponding to the position coordinates of particle i at time t, ||·|| represents the Euclidean distance, exp(·) represents the exponential function, and σ represents the standard deviation of the matching error, which is specifically set to 0.1m.

[0095] Furthermore, for auxiliary positioning in low visibility scenarios, it includes:

[0096] The effective point cloud detection distance is limited to 20 m, and the lidar acquisition frequency is adjusted to 15 Hz (haze scenario) or 20 Hz (rainy day scenario) in combination with the weather conditions;

[0097] Road markings and traffic signs are extracted from the visual data to generate auxiliary positioning feature points, which are weighted and fused with the position matching results.

[0098] In addition, for the complex scenario of an autonomous driving bus running in rainy days, since the reflected signal of the lidar by raindrops has noise interference, the camera vision is blurred, and the water on the road surface may cause the vehicle to slip, which has a certain impact on the accuracy of the vehicle motion model. The correction effect of the inertial measurement unit on the vehicle attitude is enhanced to compensate for the data error of the odometer. And the particle weights are further updated and normalized to reflect the environmental characteristics.

[0099] In this step, the lidar data is efficiently matched with the pre - built SLAM map through the NDT algorithm, and the positioning result is optimized by fusing visual feature points with weather - adaptive adjustment. It integrates multi - source data through a weighted fusion strategy to effectively cope with the problems of rainy - day radar noise, camera blur, and vehicle slip; while the dynamic particle weight update and IMU attitude correction compensate for the odometer error, significantly improving the positioning accuracy and environmental adaptability in low visibility scenarios and ensuring the stable operation of the autonomous driving bus in complex weather.

[0100] In this embodiment S4, through the position matching result, an initial static map is constructed and dynamic obstacles are detected to generate a dynamic - static fusion SLAM map;

[0101] Among them, the construction of the initial static map includes:

[0102] Project the initial point cloud onto a 2D grid map with a resolution of 0.1 m, and mark obstacles as non-passable areas; if the map construction fails, reduce the resolution to 0.2 m and increase the number of point cloud acquisitions; if it still fails, prohibit the vehicle from starting and trigger an alarm.

[0103] Furthermore, in S4, the dynamic obstacle detection includes:

[0104] Compare the current frame point cloud with the previous frame point cloud, extract dynamic obstacles through point cloud difference, and set a speed threshold for classification; specifically, for obstacle classification, when an autonomous driving bus operates on a conventional road, judge dynamic objects through the speed threshold, and set the recognition speed range for low-speed targets (pedestrians) and high-speed targets (vehicles). Remove the dynamic obstacle area from the static map and mark the position of the dynamic obstacle in the local temporary map to avoid the planned path passing through this area.

[0105] Furthermore, in the scenario of autonomous driving bus operation in obstacle-intensive areas such as urban main roads, based on lidar combined with high-resolution video for recognition, use the YOLO algorithm to identify and distinguish dynamic obstacles such as pedestrians, motor vehicles, electric bicycles, and bicycles, and adjust the priority of dynamic obstacles, giving higher weights to targets close to the autonomous driving bus; the priority weighting formula is:

[0106]

[0107] where, w priority represents the priority weight of the dynamic obstacle, d represents the Euclidean distance between the dynamic obstacle and the autonomous driving bus, ϵ represents a small constant to prevent the situation where the weight becomes infinitely large when the obstacle is infinitely close to the vehicle, and ϵ is set to 0.1 m;

[0108] Also, increase the lower limit of the dynamic obstacle speed threshold to 0.5 m / s in rainy weather scenarios, and correct the vehicle slip error through the inertial measurement unit.

[0109] In addition, if the obstacle detection is misjudged (such as a dynamic object being misidentified as static), introduce lidar time series difference for secondary detection, and if it still fails, by default, mark the suspicious area as a dangerous area and do not allow passage.

[0110] Furthermore, perform real-time map incremental update to ensure the vehicle's timely response to environmental changes; when adding the newly detected dynamic obstacle information to the map, set weights for the newly added area. The rule for setting the weight of the newly added obstacle is:

[0111]

[0112] This step accurately separates dynamic and static obstacles through point cloud difference and speed threshold classification, combines the YOLO algorithm to realize the recognition of multi-category dynamic targets such as pedestrians and vehicles, and optimizes the path planning based on the distance priority weight. It fuses lidar and visual data in dynamic obstacle detection to improve classification accuracy and real-time performance; while the static map adaptive resolution adjustment and incremental update mechanism ensure the reliability of the map; the speed threshold optimization and secondary detection mechanism in rainy weather scenarios reduce the risk of misjudgment, combined with the dangerous area marking and weight rules, significantly enhancing the dynamic response ability and driving safety in complex road environments.

[0113] In this embodiment S5, state monitoring is performed under the dynamic and static fusion SLAM map, and a multi-level fault recovery strategy is triggered.

[0114] As Figure 2 shown, the multi-level fault recovery strategy includes:

[0115] If the mean particle weight < 0.01, the sensor data loss lasts for ≥ 1 second, or the map update delay ≥ 500 ms, it is determined that the positioning fails; then reset the particle distribution to the historical highest weight state, and remove the latest dynamic obstacle marker and update it to the previous map version;

[0116] If the map update fails, enable the block storage mechanism and only update the local map within 30 meters around.

[0117] Furthermore, the multi-level fault recovery strategy also includes:

[0118] If a speed abnormal obstacle is detected, trigger a voice alarm, emergency stop and remote notification to the monitoring center.

[0119] This step triggers a multi-level recovery strategy by real-time monitoring of particle weight, sensor status and map update delay, and starts emergency stop and remote warning for speed abnormal obstacles. Its dynamic rollback mechanism quickly restores the positioning function in combination with the historical state, and the block update reduces the computational load; the multi-level fault determination and response significantly improve the system fault tolerance and real-time performance, ensuring the continuous and stable operation and active safety protection of the autonomous driving bus in complex scenarios.

[0120] This embodiment proposes a high-precision positioning method for driverless buses based on SLAM maps. Through multi-source data fusion and timestamp synchronization, a pose hypothesis set is generated by combining particle filtering, and the NDT algorithm is used to achieve efficient matching between lidar and maps. For low visibility scenarios, radar parameters are adaptively adjusted and visual data is fused to enhance positioning robustness; dynamic obstacles are accurately detected through point cloud difference, YOLO algorithm, and priority weights, and a dynamic and static fusion map is constructed. A multi-level fault recovery strategy monitors the system status in real time and quickly responds to positioning failures or abnormal obstacles. This method comprehensively improves the positioning accuracy, dynamic obstacle handling ability, and system fault tolerance in complex environments, ensuring the safe and stable operation of autonomous driving buses in adverse weather and dense scenarios.

[0121] The various embodiments in this specification are described in a progressive manner. The key point of each embodiment is to illustrate the differences from other embodiments. For the same or similar parts between the embodiments, reference can be made to each other. For the system disclosed in the embodiments, since it corresponds to the method disclosed in the embodiments, the description is relatively simple. For the relevant parts, reference can be made to the description in the method section.

[0122] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present invention. Various modifications to these embodiments will be obvious to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but will be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A high-precision positioning method for driverless buses based on SLAM maps, characterized in that, It includes the following steps: S1. Obtain the lidar data, inertial measurement data, odometer data, and visual data of the driverless bus, perform timestamp synchronization and integrity verification, and fuse the inertial measurement data and odometer data; S2. Use the fused data to parse vehicle control instructions, simulate the motion trajectory of the driverless bus through the particle filter algorithm, and generate a pose hypothesis set; S3. Based on the pose hypothesis set, perform position matching between the lidar data and the pre-constructed SLAM map, and perform visual-aided positioning in low visibility scenarios to obtain a position matching result; S4. Through the position matching result, perform initial static map construction and dynamic obstacle detection to generate a dynamic and static fusion SLAM map; S5. Perform state monitoring under the dynamic and static fusion SLAM map and trigger a multi-level fault recovery strategy.

2. A high-precision positioning method for driverless buses based on a SLAM map according to claim 1, characterized in that, In S1, the timestamp synchronization and integrity verification include: Timestamp synchronization: Taking the timestamp of the lidar data as the benchmark, align the inertial measurement data and odometer data to the same time series through the linear interpolation algorithm; Integrity verification: Verify that the number of lidar data points per frame > 500, the absolute value of the acceleration of the inertial measurement data ≤ 30 ± m / s2, and the absolute value of the odometer data ≤ 15 m / s; if the data is incomplete, supplement the missing data by combining the time interpolation algorithm.

3. A high-precision positioning method for driverless buses based on SLAM maps according to claim 1, characterized in that, In S1, the Kalman filter algorithm is used to fuse the inertial measurement data and odometer data; The state prediction equation of the Kalman filter algorithm is expressed as: x’ k =A·x’ k−1 +B·u k ; where x' k represents the state prediction value at time k, and x' k−1 represents the state prediction value at time k−1, u k represents the control input vector, A represents the state transition matrix, and B represents the control input matrix; The state update equation of the Kalman filter algorithm is expressed as: x k = x' k + K·(z k - H·x' k ); where x k represents the state update value at time k, z k represents the state measurement value at time k, K represents the Kalman gain weight matrix, and H represents the observation matrix.

4. A high-precision positioning method for driverless buses based on SLAM maps according to claim 1, characterized in that, In S2, the vehicle control instruction parsing includes: According to the speed v t and the steering angular velocity ω t , calculate the displacement increment Δx and the angle increment Δθ; and set the maximum vehicle speed to 15 m / s and the maximum steering angular velocity to ±0.5 rad / s. If the speed or the steering angular velocity exceeds the threshold, trigger the amplitude limiting constraint.

5. A high-precision positioning method for driverless buses based on SLAM maps according to claim 1, characterized in that, In S2, the calculation equation for the movement of each particle in the particle filter algorithm is: x i,t = x i,t-1 + Δx + N1(0, σ x ); θ i,t = θ i,t-1 + Δθ + N2(0, σ θ ); where x i,t represents the position coordinate of particle i at time t, and x i,t-1 represents the position coordinate of particle i at time t - 1. N1 represents position noise with a mean of 0 and a standard deviation of σ x ; θ i,t represents the angular coordinate of particle i at time t, and θ i,t-1 represents the angular coordinate of particle i at time t - 1. N2 represents angular noise with a mean of 0 and a standard deviation of σθ.

6. A high-precision positioning method for driverless buses based on SLAM maps according to claim 1, characterized in that, In S3, the position matching between the lidar data and the pre-constructed SLAM map includes: mapping the lidar data to a grid map using the NDT algorithm and calculating the position matching probability of each particle : ； Among them, z t represents the current observed point cloud, h(x i,t ) represents the predicted point cloud corresponding to the position coordinates of particle i at time t, ||·|| represents the Euclidean distance, exp(·) represents the exponential function, and σ represents the standard deviation of the matching error.

7. A high-precision positioning method for driverless buses based on SLAM maps according to claim 1, characterized in that, In S3, the visual-aided positioning in low visibility scenarios includes: Limit the effective point cloud detection distance to 20 m, and adjust the lidar acquisition frequency to 15 Hz or 20 Hz in combination with the weather conditions; Extract road markings and traffic signs from the visual data to generate visual-aided positioning feature points, and perform weighted fusion with the position matching result.

8. A high-precision positioning method for driverless buses based on SLAM maps according to claim 1, characterized in that, In S4, the initial static map construction includes: Project the initial point cloud onto a two-dimensional grid map with a resolution of 0.1 m, and mark the obstacles as non-passable areas; if the map construction fails, reduce the resolution to 0.2 m and increase the number of point cloud acquisitions; if it still fails, prohibit the vehicle from starting and trigger an alarm.

9. A high-precision positioning method for driverless buses based on SLAM maps according to claim 1, characterized in that, In S4, the dynamic obstacle detection includes: Extract dynamic obstacles through point cloud difference and set a speed threshold for classification; Use the YOLO algorithm to identify the types of obstacles in the visual data and dynamically assign weight priorities according to the distance; Moreover, increase the lower limit of the dynamic obstacle speed threshold to 0.5 m / s in rainy weather scenarios, and correct the vehicle slip error through the inertial measurement unit.

10. A high-precision positioning method for driverless buses based on SLAM maps according to claim 1, characterized in that, In S5, the multi-level fault recovery strategy includes: When the mean particle weight < 0.01, the sensor data loss lasts for ≥ 1 second, or the map update delay ≥ 500 ms, it is determined that the positioning fails; then reset the particle distribution to the state with the highest historical weight, and remove the latest dynamic obstacle markers and update to the previous map version; If the map update fails, enable the block storage mechanism and only update the local map within a range of 30 meters around.

Images