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In unstructured outdoor environments, robotics requires accurate andefficient odometry with low computational time. Existing low-bias LiDARodometry methods are often computationally expensive. To address this problem,we present a lightweight LiDAR odometry method that converts unorganized pointcloud data into a spherical range image (SRI) and filters out surface, edge,and ground features in the image plane. This substantially reduces computationtime and the required features for odometry estimation in LOAM-basedalgorithms. Our odometry estimation method does not rely on global maps or loopclosure algorithms, which further reduces computational costs. Experimentalresults generate a translation and rotation error of 0.86\% and 0.0036{\deg}/mon the KITTI dataset with an average runtime of 78ms. In addition, we testedthe method with our data, obtaining an average closed-loop error of 0.8m and aruntime of 27ms over eight loops covering 3.5Km.

LiLO: Lightweight and low-bias LiDAR Odometry method based on spherical range image filtering