融合双目视觉和 2D 激光雷达的室外定位
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作者单位:

1.北京工业大学信息学部;2.煤炭科学研究总院矿山大数据研究院

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中图分类号:

TP242

基金项目:

国家自然科学基金项目(面上项目,重点项目,重大项目)


Fusion of stereo vision and 2D LiDAR for Outdoor Localization
Author:
Affiliation:

1.Faculty of Information Technology, Beijing University of Technology;2.Research Institute of Mine Big Data, China Coal Research Institute

Fund Project:

The National Natural Science Foundation of China (General Program, Key Program, Major Research Plan)

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    摘要:

    高精度的定位对于自动驾驶系统至关重要. 2D激光雷达作为一种高精度的传感器被广泛应用于各种室内定位系统. 然而在室外环境下,大量动态目标的存在使得相邻点云的匹配变得尤为困难,且2D激光雷达的点云数据存在稀疏性的问题,导致2D激光雷达在室外环境下的定位精度极低甚至无法实现定位. 为了解决这一问题,本文提出一种融合双目视觉和2D激光雷达的定位算法,首先,利用双目视觉作为里程计提供相对位姿,将一个局部时间窗口内多个时刻得到}的2D激光雷达数据融合成一个局部子图. 同时,采用DS证据理论融合局部子图中的时态信息,从而消除动态目标带来的噪声. 最后,利用基于ICA的图像匹配方法将局部子图和预先构建的全局先验地图进行匹配,消除里程计的累积误差实现高精度定位. 在KITTI数据集上的实验结果表明,仅利用低成本的双目相机和2D激光雷达可实现较高精度的定位,所提出算法的定位精度相比ORB-SLAM2里程计最高提升了37.9%,与基于3D激光雷达的定位精度相当甚至在部分序列上更优.

    Abstract:

    Precise localization is an essential issue for autonomous driving systems. 2D LiDAR, as a high-precision sensor, is widely used in various indoor localization systems. However, in the outdoor environment, the existence of a large number of dynamic targets makes the matching of adjacent point clouds particularly difficult. Moreover, the point cloud captured by 2D LiDAR is sparse, leading to the localization accuracy of 2D LiDAR in the outdoor environment being very low or even unable to achieve localization. Therefore, a localization system fusing stereo vision and 2D LiDAR is proposed in this paper. Stereo vision is used to calculate the relative pose, so as to fuse the 2D LiDAR data in a local time window into a local submap. Dempster-Shafer evidence theory is used to fuse temporal information in local submap to eliminate noise caused by dynamic targets. Finally, the ICA-based image matching method is used to match the local submap with a pre-constructed global prior map to eliminate the cumulative error of the stereo odometry. The experimental results on KITTI dataset show that precise localization can be achieved only by using a low-cost stereo camera and 2D LiDAR. Compared with the odometry of ORB-SLAM2, the proposed localization system improves the localization performance by 37.9%. Its performance is also comparable to that of the localization system based on 3D LiDAR, and even superior to the latter on some sequences.

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  • 收稿日期:2021-11-03
  • 最后修改日期:2022-03-09
  • 录用日期:2022-03-15
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