改进路径跟踪算法在机器人SLAM中的应用研究

doi:10.16182/j.issn1004731x.joss.22-0873

摘要/Abstract

摘要：

绘制地图是自动化物流领域的重要环节，目前普遍采用即时定位与建图(SLAM)方法，但在大规模场景下，机器人常在区域边缘地带反复测扫从而积累误差，无法快速构建高精度完整地图。提出一种基于辅助路径跟踪的机器人自主建图方法，对给定的初始草图进行栅格化去噪，通过多段三次多项式对辅助路径进行拟合改进，采用改进的纯跟踪算法引导机器人建图，改善SLAM建图过程的总距离和时间。实验表明：该算法在地图完整性、准确度和建图效率方面，较现有V-SLAM、QRCode-SLAM方法均有改善，为快速高效地构建地图提供了一种可视化的双向交互途径。

关键词: SLAM, 高精度完整地图, 栅格化去噪, 辅助路径跟踪, 地图构建, 双向交互

Abstract:

Mapping is an important part of automated logistics. At present, SLAM is widely used. However, in large-scale scenes, errors are accumulated because robots often repeatedly measure and scan the region edge, which makes it impossible to quickly build a high-precision and complete map. An autonomous mapping method based on auxiliary path tracking is proposed, in which the given initial sketch is grid denoised and the auxiliary path is fitted and improved by multi segment cubic polynomial. The improved pure pursuit algorithm is used to guide the robot to build the map and improve the total distance and time of slam mapping process. Experiments in simulation and real scenes show that, compared with the existing V-SLAM and QRCode-SLAM methods, the algorithm improves the map integrity, accuracy and efficiency and provides a visual two-way interactive way for fast and efficient map construction.

Key words: SLAM, high precision complete map, denoising, auxiliary path, map construction, interaction

中图分类号:

TP242

李倩,陶冶,李辉 . 改进路径跟踪算法在机器人SLAM中的应用研究[J]. 系统仿真学报, 2023, 35(12): 2602-2613.

Li Qian,Tao Ye,Li Hui . Application of Improved Path Tracking Algorithm in Robot SLAM[J]. Journal of System Simulation, 2023, 35(12): 2602-2613.

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前瞻距离系数	最大横向误差/m	最大航向误差/rad
0.5	0.71	0.610
1.0	0.11	0.070
1.5	0.16	0.087
2.0	0.21	0.085

场景规模/m²	建图方法	用时	I/%	λ/%
10×6 (图15(a))	本文方法	37 min 25 s	100	99.82
10×6 (图15(a))	SLAM	50 min 5 s	93.65	93.21
45×22 (图16(a))	本文方法	3 h 17 min 3 s	99.82	99.05
45×22 (图16(a))	SLAM	3 h 55 min 11 s	94.09	75.45
21×22 (图17(a))	本文方法	4 h 19 min 37 s	99.33	97.70
21×22 (图17(a))	SLAM	5 h 11 min 32 s	93.70	72.41

使用方法	辅助程度	平均时间	I/%	λ/%
RRT	四边形边界	15 min 37 s	74.70	77.39
QRCode-SLAM	QRCode	11 min 49 s	83.43	79.27
V-SLAM	深度相机	9 min 32 s	70.16	63.20
V+Laser-SLAM	Laser+相机	10 min 25 s	86.96	89.32
本文方法	辅助路径	7 min 4 s	99.09	99.52

使用方法	辅助程度	平均时间	I/%	λ/%
RRT	四边形边界	11 min 32 s	67.03	59.13
QRCode-SLAM	QRCode	10 min 8 s	80.26	68.00
V-SLAM	深度相机	5 min 48 s	64.31	53.57
V+Laser-SLAM	Laser+相机	6 min 12 s	85.25	90.19
本文方法	辅助路径	4 min 15 s	98.89	97.78

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