基于LiDAR-IMU的动态场景点云建图方法

doi:10.16182/j.issn1004731x.joss.23-1078

摘要/Abstract

摘要：

针对在城市道路等动态场景下构建点云地图时易受到动态物体干扰，导致建图精度和准确性下降的问题，提出了一种基于激光雷达（LiDAR）与惯性测量单元（IMU）的动态场景点云地图构建方法。利用基于索引的八叉树体素结构来提高局部感知地图（local perception map，LP-Map）的增量更新和近邻搜索效率；通过地面分割、聚类和动态分数计算的方法对点云进行处理，实时地识别出动态目标点云；利用区域增长方法剔除LP-Map中的动态物体行驶轨迹。实验结果证明，所提方法在建图层面能有效识别并剔除场景中的动态目标，同时具备出色的计算速度和实时性能，为动态场景下实时地构建精确点云地图提供了可靠的解决途径。

关键词: 激光雷达, 惯性测量单元, 点云地图构建, 动态场景, 局部感知地图

Abstract:

In order to address the issue of decreased mapping accuracy and precision caused by dynamic object interference during the construction of point cloud maps in dynamic scenarios such as urban roads, this study proposes a method for building dynamic scene point cloud maps based on LiDAR and inertial measurement unit (IMU). The method incorporates several key steps. An index-based Octree voxel structure is utilized to enhance the incremental update and nearest neighbor search efficiency of the local perception map (LP-Map). The point cloud is processed using ground segmentation, clustering, and dynamic score calculation methods to enable real-time identification of dynamic target point clouds. The region growing method is employed to remove the trajectories of dynamic objects in the LP-Map. Experimental results demonstrate that the proposed algorithm effectively identifies and removes dynamic targets at the mapping level while exhibiting exceptional computational speed and real-time performance. This approach provides a reliable solution for real-time construction of accurate point cloud maps in dynamic scenarios.

Key words: LiDAR, IMU, construction of point cloud maps, dynamic scenarios, local perception map

中图分类号:

TP 242

李维刚,甘磊,王永强 . 基于LiDAR-IMU的动态场景点云建图方法[J]. 系统仿真学报, 2025, 37(1): 95-106.

Li Weigang,Gan Lei,Wang Yongqiang . Dynamic Scene Point Cloud Mapping Method Based on LiDAR-IMU[J]. Journal of System Simulation, 2025, 37(1): 95-106.

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数据集序列	帧数	时间/s	动态等级
Street_1	248	25	高
Street_2	284	28	高
Cross_1	392	40	低
Cross_2	357	35	低

序列	动态程度	Method	TP	FN	FP	R	P	Runtime/iteration/s
Street_1		Removert	12 088	208	2 050	0.983	0.855	0.820 8
	高	ERASOR	11 968	356	764	0.971	0.940	0.141 2
		Ours	11 694	602	1 312	0.951	0.899	0.064 2
Street_2		Removert	6 745	223	1 362	0.968	0.832	0.894 5
	高	ERASOR	6 658	277	934	0.960	0.877	0.165 2
		Ours	6 542	244	1 056	0.964	0.861	0.076 1
Cross_1		Removert	5 548	529	1 217	0.913	0.820	0.604 3
	低	ERASOR	5 614	475	582	0.922	0.906	0.095 4
		Ours	5 726	387	542	0.937	0.914	0.031 3
Cross_2		Removert	15 968	2 198	2 136	0.879	0.882	0.746 1
	低	ERASOR	16 394	650	2 214	0.933	0.881	0.104 6
		Ours	16 744	1 030	1 798	0.942	0.903	0.054 7
		Removert	3 671	145	643	0.962	0.850	1.156 4
Seq_01	高	ERASOR	3 726	159	743	0.959	0.834	0.218 6
		Ours	4 027	224	685	0.947	0.865	0.086 4
		Removert	8 613	634	937	0.931	0.902	0.864 4
Seq_02	低	ERASOR	8 264	603	863	0.932	0.905	0.154 6
		Ours	8 936	537	738	0.943	0.924	0.071 5
		Removert	14 273	1 902	1 836	0.882	0.886	0.785 2
Seq_03	高	ERASOR	14 382	1 003	1 761	0.935	0.891	0.135 4
		Ours	14 532	1 382	1 627	0.913	0.899	0.046 4

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