基于激光雷达SLAM的三维点云自适应算法

3D point cloud adaptive algorithm based on LiDAR SLAM

SLAM(Simultaneously Localization And Mapping)同步定位与地图构建作为移动机器人智能感知的关键技术。但是,大多已有的SLAM方法是在静止环境下实现的,当环境中存在移动频繁的障碍物时,SLAM建图会产生运动畸变,导致机器人无法进行精准的定位导航。同时,激光雷达等三维扫描设备获得的三维点云数据存在着大量的冗余三维数据点,过多的冗余数据不仅浪费大量的存储空间,同时也影响了各种点云处理算法的实时性。针对以上问题,本文提出一种SLAM运动畸变去除方法和一种基于曲率的点云数据分类简化框架。它通过激光插值法优化SLAM运动畸变,将优化后的点云数据分类简化。它能在提高SLAM建图精度,同时也很好的消除三维点云数据中特征不明显区域的冗余数据点,大大提高计算机运行效率。

SLAM(Simultaneous Localization and Mapping)synchronous positioning and map construction is the key technology of intelligent perception of mobile robots.However,most of the existing SLAM methods are implemented in stationary environments,and when there are frequently moving obstacles in the environment,SLAM mapping will produce motion distortion,resulting in the robot being unable to accurately locate and navigate.Meanwhile,there are a large number of redundant 3D data points in the 3D point cloud data obtained by 3D scanning equipment such as LiDAR,and excessive redundant data not only wastes a large amount of storage space,but also affects the real-time performance of various point cloud processing algorithms.To address the above problems,a laser SLAM motion distortion removal method and a curvature-based point cloud data classification simplification framework is proposed in this paper.It optimizes SLAM motion distortion by laser interpolation,simplifying the classification of optimized point cloud data.It can improve the accuracy of SLAM mapping,and also well eliminate the redundant data points in the 3D point cloud data with unclear features,greatly improving the efficiency of computer operation.