Lidar simultaneous localization and mapping with low-drift

LIU Li-xin; SUN Wei; WANG Hai-chao

doi:10.3788/OPE.20172513.0052

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Lidar simultaneous localization and mapping with low-drift

更新时间：2020-08-13

- Lidar simultaneous localization and mapping with low-drift
- Optics and Precision Engineering Vol. 25, Issue 10s, Pages: 52-59(2017)
- 作者机构：
  
  西安电子科技大学空间科学与技术,陕西西安,710118
- 作者简介：
- 基金信息：
- DOI：10.3788/OPE.20172513.0052
  CLC： V243.2
- Received：01 June 2017，
  
  Revised：22 June 2017，
  
  Published：25 November 2017
- 稿件说明：
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刘立新, 孙伟, 汪海潮. 具有低漂移度的激光雷达同步定位和三维建图[J]. 光学精密工程, 2017,25(10s): 52-59

LIU Li-xin, SUN Wei, WANG Hai-chao. Lidar simultaneous localization and mapping with low-drift[J]. Editorial Office of Optics and Precision Engineering, 2017,25(10s): 52-59
刘立新, 孙伟, 汪海潮. 具有低漂移度的激光雷达同步定位和三维建图[J]. 光学精密工程, 2017,25(10s): 52-59 DOI： 10.3788/OPE.20172513.0052.

LIU Li-xin, SUN Wei, WANG Hai-chao. Lidar simultaneous localization and mapping with low-drift[J]. Editorial Office of Optics and Precision Engineering, 2017,25(10s): 52-59 DOI： 10.3788/OPE.20172513.0052.

摘要

鉴于传统的2D激光雷达在不借助其他传感器的情况下难以实时绘制精细的三维地图，本文提出以六自由度移动2D激光雷达，通过其在扫描期间感知的点云执行运动估计，构建扫描环境的三维地图。首先，以程序开始时雷达所在位置为世界坐标系的原点，将雷达扫描到的激光点与电机的旋转角度结合，使激光点带有三维坐标信息，并注册到激光雷达坐标系中。使用双边滤波算法对雷达坐标系中的点云进行滤波，然后采用激光雷达测距算法处理点云数据，计算连续两次扫描之间的运动。估算的运动用于雷达自身的姿态变换和矫正点云失真。激光雷达绘图算法进一步处理矫正后的点云并配准到地图上输出三维点云图，同时输出雷达的姿态变换。最后整合两种算法的姿态变换，输出激光雷达在世界坐标系中的位置信息。实验结果表明：该方法可以使用激光雷达原始数据和电机位置信息实时地精确估计激光雷达以六自由度运动的轨迹，同时构建高质量的激光雷达三维点云图。提出的方法具有良好的实时性，室内环境中测试的相对精度约为2%，适用于三维地图的在线重建。

Abstract

As conventional 2D lidars can't reconstruct three-dimensional map in real time without other sensors

this paper constructed a three-dimensional map from point clouds perceived by a 2D laser lidar scanning in six degrees of freedom. First

the location of radar was taken as the origin of world coordinate system at the begging of the program. The laser spot scanned by the radar was combined with the rotation angle of the motor so that the laser point has three-dimensional coordinate information

which was registrated into the radar coordinate system. The bilateral filtering algorithm was adopted to filter the point cloud

which was then processed by lidar ranging algorithms to calculate the motion of the lidar between two successive scans. The estimated motion was used to correct the distortion of the point cloud. The drawing algorithm further processes the output of the ranging algorithm at a lower frequency

fine-graining the corrected cloud to the map. Finally

the two algorithms were integrated to map and post the radar posture transformation in real time

thus outputting the position of lidar in world coordinate system. Experimental results show that the method can accurately estimate the trajectory of the lidar with six degrees of freedom in real time based on the laser radar original data and the motor position information

and meanwhile construct high-quality 3D point cloud image. The proposed method exhibits good real-time performance

and the relative precision of the indoor test is approximately 2%.

关键词

Keywords

references

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