动态环境下融合激光雷达和IMU的激光里程计设计

激光SLAM 通常通过多帧点云配准,完成位姿变换矩阵的估计,而点云中的动态点会降低 激光里程计的定位精度。为了减少动态点引入的误差,提出了一种动态点云识别算法,并结合该 算法改进了传统特征匹配策略,组成了动态环境下融合激光雷达和IMU 的激光里程计。通过约 束范围角与动态中心点,将点云快速分割成多个簇类,再借助IMU 信息,快速建立点云簇类配准 关系,从而去除动态点,最后根据簇类的对应关系进行约束,以提高匹配的精度与速度。使用 KITTI数据集和UGV 在多个情形下进行了实验。实验结果表明,该算法可以成功识别点云中的 多个动态对象,并通过去除动态点,有效地减少了位姿估计的累积误差。

Design of Lidar Odemeter Integrating Lidar and IMU in Dynamic Environment

Multi-framepointcloudregistrationisusuallyusedtoestimatetheposetransformation matrix.Thedynamicpointsinthepointcloudreducethepositioningaccuracyofthelidarodometer. Inordertoreducetheerrorintroducedbydynamicpoints,adynamicpointcloudrecognition algorithmisproposed,andcombinedwiththisalgorithm,thetraditionalfeaturematchingstrategy isimprovedtoformalaserodometerthatintegrateslidarandIMUinadynamicenvironment.The pointcloudisquicklydividedintomultipleclustersbyrestrictingtherangeangleandthedynamic centerpoint,andthenusingtheIMUinformation,theregistrationrelationshipofthepointcloud clustersisquicklyestablished.Therebythedynamicpointsareremoved,andfinallytheprecision andspeedofmatchingareimprovedbyconstrainingaccordingtothecorrespondingrelationshipof theclusters.ExperimentsarecarriedoutinmultiplesituationsbyusingtheKITTIdatasetand UGV.Theresultsshowthatthedesigneffectivelyreducethecumulativeerrorofposeestimation byremovingdynamicpoints.