基于规划路径约束的机器人定位方法

胡钊政; 许聪; 周哲; 邓泽武

doi:10.11999/JEIT210984

基于规划路径约束的机器人定位方法

doi: 10.11999/JEIT210984

胡钊政^{1, 2, ,},
许聪^{1, 2},
周哲^{2, 3},
邓泽武^{1, 2}

1.
武汉理工大学信息工程学院武汉 430070
2.
武汉理工大学智能交通系统研究中心武汉 430063
3.
武汉理工大学重庆研究院重庆 401120

基金项目: 国家自然科学基金(U1764262)，武汉市科学技术局企业技术创新项目(2020010601012165, 2020010602011973, 2020010602012003)，武汉理工大学重庆研究院科技创新研发项目(YF2021-04)

详细信息

作者简介:
胡钊政：男，教授，研究方向为3D计算机视觉理论与应用、智能车路系统、视觉与激光SLAM定位等

许聪：男，硕士生，研究方向为激光SLAM定位、机器人定位、多传感器融合定位等

周哲：男，博士生，研究方向为基于视觉的机器人定位、多传感器融合定位等

邓泽武：男，硕士生，研究方向为激光SLAM定位、机器人定位、视觉定位等

通讯作者:
胡钊政　zzhu@whut.edu.cn

中图分类号: TP242
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- 文章访问数: 870
- HTML全文浏览量: 140
- PDF下载量: 130
- 被引次数: 0
出版历程
- 收稿日期: 2021-09-15
- 修回日期: 2022-04-07
- 网络出版日期: 2022-04-22
- 刊出日期: 2022-11-14

Robot Localization Based on Planned Path Constraints

HU Zhaozheng^{1, 2
, ,},
XU Cong^{1, 2},
ZHOU Zhe^{2, 3},
DENG Zewu^{1, 2}

1.
School of Information Engineering, Wuhan University of Technology, Wuhan 430070, China
2.
Intelligent Transportation Systems Research Center, Wuhan University of Technology, Wuhan 430063, China
3.
Chongqing Research Institute, Wuhan University of Technology, Chongqing 401120, China

Funds: The National Natural Science Foundation of China(U1764262), The Enterprise Technology Innovation Project of Wuhan Science and Technology Bureau(2020010601012165, 2020010602011973, 2020010602012003), The Scientific and Technological Innovation Research and Development Project of Chongqing Research Institute of Wuhan University of Technology(YF2021-04)

摘要

摘要: 路径规划是为机器人生成可行驶路径以实现循迹的过程。因此，机器人的位置应该位于或靠近规划的行驶路径。从而，路径规划可为机器人定位产生重要的约束。该文提出一种规划路径约束的位置概率图 (PI-LPM)模型，该模型通过概率来表征机器人在整个地图范围内所处的位置的可能性。其中，模型中概率密度函数是通过核密度估计 (KDE)方法从表征规划路径的所有数据点生成。在所提出的PI-LPM模型基础上，提出一种规划路径约束的机器人定位新算法 (RL-PPC)来提高机器人定位精度。在该方法中，应用粒子滤波算法来融合所提出的PI-LPM模型和已有的传感器定位方法。融合过程中，从PI-LPM模型中计算得到的概率是分配粒子权重的一个重要因素。实验中分别利用仿真数据和真实数据对所提出的模型与算法进行验证。实验结果表明，所提RL-PPC算法可有效融合PI-LPM模型与主流的定位系统(如GPS和LiDAR定位系统)，并显著提高机器人定位的整体性能。
- 机器人定位 /
- 概率图模型 /
- 路径规划 /
- 核密度估计 /
- 粒子滤波
Abstract: Path planning is a step to generate a feasible path for a robot to track along. Locations of the robot are supposed to lie on or at least nearby the planned path, which can thus generate important constraints for robot localization. In this paper, a model, called Path-Induced Location Probability Map (PI-LPM), to exploit such constraint on robot localization is proposed. The proposed PI-LPM model is a Probability Density Function (PDF) over the entire map with the probability to describe the likelihood that the robot is located. The PDF is generated from all the points representing the path by applying the Kernel Density Estimation (KDE) method with each point as a sampling point. Based on the PI-LPM model, a Robot Localization from Planned Path Constraints (RL-PPC) method to enhance robot localization is proposed. In this method, particle filter is applied to fuse the develop PI-LPM model and existing localization methods, where the probability from PI-LPM is an important factor to assign weights to the particles. The proposed method is validated with both simulation and real data. In the experiment, the proposed PI-LPM model is integrated into both GPS and LiDAR based localization systems. Experimental results demonstrate that the RL-PPC method can effectively improve the over-all performance of robot localization.
- Robot localization /
- Probability map model /
- Path planning /
- Kernel Density Estimation (KDE) /
- Particle filter

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图 1 算法流程图

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图 2 栅格地图的绘制

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图 3 半椭圆形轨迹定位结果

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图 5 “S”形轨迹定位结果

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图 4 圆形轨迹定位结果

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图 6 二次规划后的“S”形轨迹定位结果

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图 7 移动机器人及其搭载的激光雷达

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图 8 场景1定位结果

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图 9 场景2定位结果

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图 10 场景1二次规划定位结果

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表 1 不同轨迹下RL-PPC方法定位误差对比

轨迹	最大误差(m)		平均误差(m)		误差1 m内概率(%)
轨迹	GPS	RL-PPC	GPS	RL-PPC	GPS	RL-PPC
半椭圆	3.6545	2.0103	1.1822	0.5681	45.19	86.16
圆	3.6837	1.6219	1.1595	0.5316	44.78	88.57
“S”形	3.4489	1.9076	1.1742	0.6107	48.14	86.08

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表 2 “S”形轨迹二次规划前后RL-PPC定位误差对比

轨迹	最大误差(m)	平均误差(m)	误差1 m内概率(%)
一次规划“S”形	1.9076	0.6107	86.08
二次规划“S”形	1.9044	0.6889	90.54

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表 3 定位误差与定位耗时对比

方法	定位耗时(ms/次)	最大误差(m)	平均误差(m)
文献[18] 文献[18]+本文融合算法	94 112	1.79 0.77	1.06 0.47
文献[12] 文献[12]+本文融合算法	56 65	1.60 0.73	0.96 0.45

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