# LiODOM: Adaptive Local Mapping for Robust LiDAR-Only Odometry

arXiv: https://arxiv.org/abs/2111.03393 · HTML: https://mlsanity.com/paper/2111.03393/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Nov 2021.

## Built on by (most cited first, 1 of 1)

- [ADA-DPM: A Neural Descriptors-based Adaptive Noise Filtering Strategy for SLAM](https://mlsanity.com/paper/2506.18016/) (2025)

## Abstract (arXiv)

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.