Dynamic Scene Point Cloud Mapping Method Based on LiDAR-IMU

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

Abstract

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

CLC Number:

TP 242

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.

Figures/Tables 13

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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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