LiODOM: Adaptive Local Mapping for Robust LiDAR-Only Odometry

TL;DR

LiODOM introduces an adaptive, LiDAR-only odometry and mapping method that uses a novel local map representation and efficient data structure for robust, real-time pose estimation, validated on public datasets and aerial platforms.

Contribution

The paper presents a new LiDAR-only odometry and mapping approach with a novel local map representation and fast data access scheme, improving robustness and efficiency.

Findings

LiODOM outperforms existing methods on public datasets.

It achieves accurate pose estimation in real-time.

Validated on aerial platform data.

Abstract

In the last decades, Light Detection And Ranging (LiDAR) technology has been extensively explored as a robust alternative for self-localization and mapping. These approaches typically state ego-motion estimation as a non-linear optimization problem dependent on the correspondences established between the current point cloud and a map, whatever its scope, local or global. This paper proposes LiODOM, a novel LiDAR-only ODOmetry and Mapping approach for pose estimation and map-building, based on minimizing a loss function derived from a set of weighted point-to-line correspondences with a local map abstracted from the set of available point clouds. Furthermore, this work places a particular emphasis on map representation given its relevance for quick data association. To efficiently represent the environment, we propose a data structure that combined with a hashing scheme allows for fast access to any section of the map. LiODOM is validated by means of a set of experiments on public datasets, for which it compares favourably against other solutions. Its performance on-board an aerial platform is also reported.