基于强度信息特征滤波的激光SLAM算法

doi:10.16182/j.issn1004731x.joss.23-1166

摘要/Abstract

摘要：

为解决过多的特征点参与点云配准导致算法精度下降和建图效果不佳问题，提出一种基于强度信息特征滤波的激光SLAM算法。根据点云强度信息计算局部地图中特征点附近的强度分布情况，并为局部地图中的每个特征点赋予一个强度分布指标；通过设定强度阈值，滤除在连续帧中强度信息变化较大的无效特征点，筛选出适于将扫描帧与局部地图进行配准的有效特征点。提出一种强度加权代价函数，以获取机器人当前帧在全局地图中更准确的位姿。仿真结果表明：相较PFilter算法，每一扫描帧中的特征点数量平均减少了13.8%，局部地图中的特征点数量平均减少了18.8%，配准精度提高了5.7%。

关键词: 同步定位与建图, 激光雷达, 强度信息, 特征滤波, 点云配准

Abstract:

In order to solve the problem that an excessive influx of feature points into the point cloud registration phase can potentially lead to diminished algorithmic accuracy and suboptimal mapping outcomes, a novel laser SLAM algorithm predicated on the filtering of feature points through the utilization of intensity information is proposed. The intensity distribution near the feature points in the local map is calculated based on the point cloud intensity information, and each feature point within the local map is attributed an intensity distribution index. Through the application of an intensity threshold, feature points that exhibit substantial variations in intensity across successive frames are systematically removed. This process identifies and retains only valid feature points amenable for the registration of scanned frames with the local maps. A novel intensity-weighted cost function is proposed. This function aims to enhance the accuracy of the robot frame's pose estimation within the global map. The simulation results show that it achieves an average reduction of 13.8% in feature point count per scan frame, along with an average decrease of 18.8% in the number of feature points within the local map, enhances registration accuracy by 5.7%.

Key words: simultaneous localization and mapping, LiDAR, intensity information, feature filtering, point cloud registration

中图分类号:

TP242

李维刚,邹少峰,王永强等 . 基于强度信息特征滤波的激光SLAM算法[J]. 系统仿真学报, 2025, 37(2): 392-403.

Li Weigang,Zou Shaofeng,Wang Yongqiang,et al . Intensity-based Feature Filtering for LiDAR-based SLAM[J]. Journal of System Simulation, 2025, 37(2): 392-403.

图/表 15

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表1

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图10

表4

表5

KITTI数据集上激光雷达里程计系统建图结果

$θ i$	NFP-SF	Δ/%	NFP-LFM	Δ/%	RMSE
0.5	1 940	29.7	39 579	56.2	4.1
1.0	1 922	30.3	38 665	57.3	3.9
1.25	1 884	31.7	38 636	57.3	3.6
1.5	1 857	32.7	39 245	56.6	4.3
2.0	1 780	35.5	41 474	54.2	4.6

表5

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序号	F-LOAM	PFilter	Δ/%	IF-LOAM	Δ/%
平均值	3 391.9	2 707.2	19.3	2 338.8	30.5
KITTI-00	2 758	2 340	15.2	1 884	31.7
KITTI-01	4 127	2 538	38.5	2 231	45.9
KITTI-02	2 946	2 481	15.8	2 102	28.7
KITTI-03	3 927	3 083	21.5	2 678	31.8
KITTI-04	3 749	2 957	21.1	2 541	32.2
KITTI-05	3 056	2 557	16.3	2 173	28.9
KITTI-06	4 596	3 657	20.4	3 248	29.3
KITTI-07	2 770	2 378	14.2	2 059	25.7
KITTI-08	3 325	2 635	20.8	2 296	31.0
KITTI-09	3 384	2 757	18.5	2 417	28.6
KITTI-10	2 673	2 396	10.4	2 097	21.6

序号	F-LOAM	PFilter	Δ/%	IF-LOAM	Δ/%
平均值	87 329.4	45053.6	48.4	36605.5	57.9
KITTI-00	90 465	50 345	44.3	38 636	57.3
KITTI-01	84 775	30 273	64.3	33 781	60.2
KITTI-02	72 660	42 494	41.5	32 325	55.5
KITTI-03	91 788	47109	48.7	38158	58.4
KITTI-04	78 773	33086	58.0	31087	60.5
KITTI-05	90 940	52487	42.3	39704	56.3
KITTI-06	90 241	44861	50.3	37377	58.6
KITTI-07	90 062	47752	47.0	38205	57.6
KITTI-08	109 777	56327	48.7	42320	61.4
KITTI-09	86 714	47100	45.7	36254	58.2
KITTI-10	74 428	43756	41.2	34813	53.2

序号	LOAM	LeGO-LOAM	F-LOAM	PFilter	IF-LOAM
平均值	15.9	14.0	4.7	4.0	3.8
KITTI-00	19.4	6.3	6.5	4.2	3.6
KITTI-01	21.0	119.4	17.4	17.7	16.7
KITTI-02	111.6	14.7	14.1	8.4	8.7
KITTI-03	1.0	0.9	1.0	0.9	0.9
KITTI-04	0.5	0.8	0.4	0.4	0.4
KITTI-05	4.6	2.8	3.7	3.7	3.6
KITTI-06	1.1	0.8	0.8	0.7	0.6
KITTI-07	1.3	0.7	0.7	0.6	0.5
KITTI-08	6.7	3.5	4.6	4.6	3.8
KITTI-09	5.3	2.1	1.5	1.5	1.4
KITTI-10	1.9	1.8	1.3	1.2	1.1

算法	Street_03	Street_05	平均值
F-LOAM	32.5	0.5	16.5
PFilter	22.8	0.7	11.7
IF-LOAM	20.8	0.5	10.6

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