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PaGO-LOAM: Robust Ground-Optimized LiDAR Odometry | | Dong-Uk Seo
; Hyungtae Lim
; Seungjae Lee
; Hyun Myung
; | | Date: |
1 Jun 2022 | | Abstract: | Numerous researchers have conducted studies to achieve fast and robust
ground-optimized LiDAR odometry methods for terrestrial mobile platforms. In
particular, ground-optimized LiDAR odometry usually employs ground segmentation
as a preprocessing method. This is because most of the points in a 3D point
cloud captured by a 3D LiDAR sensor on a terrestrial platform are from the
ground. However, the effect of the performance of ground segmentation on LiDAR
odometry is still not closely examined. In this paper, a robust
ground-optimized LiDAR odometry framework is proposed to facilitate the study
to check the effect of ground segmentation on LiDAR SLAM based on the
state-of-the-art (SOTA) method. By using our proposed odometry framework, it is
easy and straightforward to test whether ground segmentation algorithms help
extract well-described features and thus improve SLAM performance. In addition,
by leveraging the SOTA ground segmentation method called Patchwork, which shows
robust ground segmentation even in complex and uneven urban environments with
little performance perturbation, a novel ground-optimized LiDAR odometry is
proposed, called PaGO-LOAM. The methods were tested using the KITTI odometry
dataset. extit{PaGO-LOAM} shows robust and accurate performance compared with
the baseline method. Our code is available at
this https URL | | Source: | arXiv, 2206.00266 | | Services: | Forum | Review | PDF | Favorites | |
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