4.6 Article

Quadric Representations for LiDAR Odometry, Mapping and Localization

期刊

IEEE ROBOTICS AND AUTOMATION LETTERS
卷 8, 期 8, 页码 5023-5030

出版社

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LRA.2023.3290510

关键词

SLAM; mapping; localization

类别

向作者/读者索取更多资源

Current LiDAR odometry, mapping, and localization methods based on point-wise representations of 3D scenes face space-inefficiency issues. To address this, we propose a novel method that describes scenes using compact quadric surface representations instead of point clouds. Our method segments the point cloud into patches and fits each patch to a quadric implicit function, providing a more efficient representation. We also introduce an incremental growing method that eliminates the need for repeated fitting. Experimental results demonstrate that our method achieves competitive accuracy with low latency and memory usage.
Current LiDAR odometry, mapping and localization methods leverage point-wise representations of 3D scenes and achieve high accuracy in autonomous driving tasks. However, the space-inefficiency of methods that use point-wise representations limits their development and usage in practical applications. In particular, scan-submap matching and global map representation methods are restricted by the inefficiency of nearest neighbor searching (NNS) for large-volume point clouds. To improve space-time efficiency, we propose a novel method of describing scenes using quadric surfaces, which are far more compact representations of 3D objects than conventional point clouds. Our method first segments a given point cloud into patches and fits each of them to a quadric implicit function. Each function is then coupled with other geometric descriptors of the patch, such as its center position and covariance matrix. Collectively, these patch representations fully describe a 3D scene, which can be used in place of the original point cloud and employed in LiDAR odometry, mapping and localization algorithms. We further design a novel incremental growing method for quadric representations, which eliminates the need to repeatedly re-fit quadric surfaces from the original point cloud. Extensive odometry, mapping and localization experiments on large-volume point clouds in the KITTI and UrbanLoco datasets demonstrate that our method maintains low latency and memory utility while achieving competitive, and even superior, accuracy.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.6
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据