基于激光点云NDT特征的两步回环检测
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国家自然科学基金项目(No.61871039;No.61871038);北京联合大学人才强校优选计划项目(No.BPHR2017EZ02)资


Two-step loop closure detection based on laser point cloud NDT features
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    摘要:

    针对传统激光雷达配准算法进行大规模同时定位与建图(SLAM)时,存在较大累计误差的问题,本文提出一种基于正态分布变换(NDT)的两步回环检测方法,充分利用NDT配准中点云均值与方差特征,并将所提方法加入SLAM完整框架。点云匹配中采用重叠网格,首先根据各网格特征值,构建点云外观描述,进行粗回环检测。符合粗回环条件后,计算点云网格均值到坐标原点距离的方法使点云具有旋转不变性,进行精确回环检测。本文提出算法在“小旋风”智能车平台进行验证,实验表明,所提算法可以有效减小大规模建图中的累计误差,系统的鲁棒性更强,跟踪性能更好。

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    Since the traditional algorithm will cause great deviation when perform large-scale simultaneous mapping and localization.A two-step loop closure detection method based on normal distribution transformation(NDT) is proposed in this paper,which makes full use of NDT to register the midpoint cloud mean and variance characteristics,and adds the proposed method to the SLAM complete framework.Point cloud registration using overlapping grids.Firstly,a point cloud appearance description based on each grid feature value to implement coarse loop closure detection is constructed.If the coarse loop closure condition is met,in order to make the point cloud have rotation invariance,the distance between point cloud grid mean and coordinate origin is calculated to realize accurate loop detection.The proposed algorithm is verified on “small cyclone” autonomous car platform.Experiments show that the algorithm can effectively reduce the cumulative deviation in large-scale construction,the robustness of the system becomes stronger,and the tracking performance becomes better.

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柴梦娜,刘元盛,任丽军.基于激光点云NDT特征的两步回环检测[J].激光与红外,2020,50(1):17~24

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  • 在线发布日期: 2020-02-13
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