高适应性激光雷达SLAM  

High Adaptive Lidar Simultaneous Localization and Mapping

作　　者：黄瑞 张轶[1] HUANG Rui;ZHANG Yi(College of Computer Science,Sichuan University,Chengdu 610065)

机构地区：[1]四川大学计算机学院,成都610065

出　　处：《电子科技大学学报》2021年第1期52-58,共7页Journal of University of Electronic Science and Technology of China

摘　　要：时定位与地图构建(SLAM),或同步建图与定位,目前主要应用于自动驾驶及机器人自主导航领域。由于激光雷达SLAM系统具有较高的测量准确性、对光照变化不敏感的特点,其在工业界获得了广泛的应用。但是基于激光雷达的SLAM算法有几个难以处理的问题:1)在结构化信息较少或在变化场景下的定位的不准确性;2)对于运动畸变的矫正能力还比较欠缺。该文针对以上问题做了两点改进:1)当结构化信息较少时,改进了原始算法处理迭代退化的步骤,提出了静态门限与动态门限结合共同应对退化的方法;2)在应对剧烈运动时,在1的基础上增加了一个局部地图优化的方法,给后端提供更精确的初始位姿和点云信息。实验结果表明,本文算法在剧烈运动和变化环境中有较好的鲁棒性及定位精度,同时免除了多传感器融合在SLAM中面临的激励不足的问题。Simultaneous localization and mapping(SLAM)is mainly used in the field of automatic driving and robot autonomous navigation.Lidar SLAM system is widely used in industry because of its high measurement accuracy and insensitivity to light change.But the SLAM algorithm based on lidar has several problems to deal with,such as:1)the inaccuracy of localization in less structured information or in changing scenes;2)the poor ability to correct motion distortion.In this paper,we make two improvements for the above problems:1)in the case of less structured information,the steps of the original algorithm in dealing with iterative degradation are improved,and a method of combining static threshold and dynamic threshold is proposed for dealing with degradation;2)in the case of intense exercise,the back-end local map on the basis of 1 is reused to provide ICP with more accurate initial pose and point cloud information.The experimental results show that proposed algorithm has good robustness and localization accuracy in severe motion condition and changing environment,and it also avoids the problem of insufficient incentive in SLAM system.

关 键 词：退化 动态门限 运动畸变 时定位与地图构建 

分 类 号：TP242.6[自动化与计算机技术—检测技术与自动化装置]