Efficient and Probabilistic Adaptive Voxel Mapping for Accurate Online LiDAR Odometry

TL;DR

This paper introduces an efficient, probabilistic adaptive voxel mapping technique for LiDAR odometry that improves accuracy and speed by using a novel voxel organization and probabilistic environment representation, validated on KITTI and outdoor datasets.

Contribution

The paper presents a new voxel map structure organized by hash tables and octrees for efficient environment mapping and integrates it with a probabilistic pose estimation framework for LiDAR odometry.

Findings

High accuracy and efficiency demonstrated on KITTI dataset

Effective in unstructured outdoor environments

Open-source code and dataset provided

Abstract

This paper proposes an efficient and probabilistic adaptive voxel mapping method for LiDAR odometry. The map is a collection of voxels; each contains one plane (or edge) feature that enables the probabilistic representation of the environment and accurate registration of a new LiDAR scan. We further analyze the need for coarse-to-fine voxel mapping and then use a novel voxel map organized by a Hash table and octrees to build and update the map efficiently. We apply the proposed voxel map to an iterated extended Kalman filter and construct a maximum a posteriori probability problem for pose estimation. Experiments on the open KITTI dataset show the high accuracy and efficiency of our method compared to other state-of-the-art methods. Outdoor experiments on unstructured environments with non-repetitive scanning LiDARs further verify the adaptability of our mapping method to different environments and LiDAR scanning patterns. Our codes and dataset are open-sourced on Github