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

TL;DR

This paper introduces LiLO, a lightweight LiDAR odometry method that converts point clouds into spherical range images, filtering features to achieve low-bias, accurate, and computationally efficient odometry without relying on global maps or loop closure.

Contribution

LiLO is a novel low-bias LiDAR odometry approach that reduces computation by using spherical range image filtering and does not depend on global maps or loop closure techniques.

Findings

Achieves 0.86% translation and 0.0036°/m rotation error on KITTI

Runs at 78ms per frame, suitable for real-time applications

Maintains low error over 3.5 km with 8 loops in real-world tests

Abstract

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