IMU-LiDAR integrated SLAM technology for unmanned driving in mines
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摘要: 同时定位与地图构建（SLAM）是无人驾驶关键技术，现有SLAM技术在煤矿巷道环境下存在累计误差大、漂移等问题。提出一种巷道环境特征辅助的惯性测量单元（IMU）与激光雷达融合SLAM算法。利用IMU观测数据预测点云运动状态并进行运动补偿，减少由设备运动引起的点云畸变；通过点云配准得到雷达里程计位姿变换信息，构成雷达里程计约束；提取巷道侧壁和地面点云并进行平面拟合，构成环境约束；基于IMU预积分约束、雷达里程计约束和环境约束，采用因子图优化方法完成激光雷达与IMU紧耦合，实现对巷道三维场景的高精度重建和无人驾驶车辆定位。仿真实验表明，巷道环境特征辅助的IMU与激光雷达融合SLAM算法的绝对轨迹均方根误差为0.116 2 m，相对轨迹均方根误差为0.040 9 m，定位精度较常用的LeGO−LOAM算法和LIO−SAM算法有所提升。真实环境测试结果表明，该算法具有良好的建图效果，未出现漂移和拖尾现象，具有较强的环境适应性和鲁棒性。Abstract: Simultaneous localization and mapping (SLAM) is a critical technology for unmanned driving. Existing SLAM methods have the drawbacks of significant cumulative errors and drift in coal mine roadway environment. In this study, a roadway environment feature-assisted SLAM algorithm integrating inertial measurement unit (IMU) and LiDAR was proposed. IMU observation data was used to predict the motion state of point cloud and motion compensation was applied to reduce point cloud distortion caused by equipment movement. Pose transformation information from LiDAR odometry was obtained through point cloud registration, forming a LiDAR odometry constraint. Point clouds from roadway sidewalls and floor were extracted and fitted to planes, establishing environmental constraints. Using IMU pre-integration constraints, LiDAR odometry constraints, and environmental constraints, the algorithm applied factor graph optimization to achieve tight coupling between LiDAR and IMU, enabling high-precision 3D reconstruction of roadway scenes and accurate localization of autonomous vehicles. Simulation experiments showed that the absolute trajectory root mean square error (RMSE) of the roadway environment feature-assisted IMU-LiDAR integrated SLAM algorithm was 0.1162 m, and the relative trajectory RMSE was 0.0409 m, improving positioning accuracy compared to commonly used algorithms such as LeGO-LOAM and LIO-SAM. Based on the test results in a real environment, the algorithm provides excellent mapping performance with no drift or trailing, demonstrating strong environmental adaptability and robustness.
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图 1 IMU与激光雷达融合SLAM算法框架
Figure 1. Framework of simultaneous localization and mapping(SLAM) algorithm integrating inertial measurement unit(IMU) and LiDAR
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图 8 3种算法规划的二维轨迹对比
Figure 8. Comparisons of 2D trajectories planned by three algorithms
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图 12 3种算法在巷道环境中的建图效果
Figure 12. Mapping results of three algorithm in readway environment
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图 14 涵洞和地下车库场景中建图效果
Figure 14. Mapping effect in culvert and underground garage scenarios
表 1 3种算法的绝对轨迹误差
Table 1 Absolute trajectory error （ATE） of three algorithms
m 指标 LeGO−LOAMS算法 LIO−SAM算法 本文算法 均方根误差 3.2237 1.9166 0.1162 平均值 1.4160 1.2771 0.1053 中值 0.7579 0.5030 0.1052 标准差 2.8961 1.4291 0.0495 
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表 2 3种算法的相对轨迹误差
Table 2 Relative pose error （RPE） of three algorithms
m 指标 LeGO−LOAM算法 LIO−SAM算法 本文算法 均方根误差 1.4924 0.3303 0.0409 平均值 0.3627 0.1236 0.0298 中值 1.0193 0.2061 0.0254 标准差 1.4476 0.3063 0.0283
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