LiODOM: Adaptive Local Mapping for Robust LiDAR-Only Odometry
This work addresses robust self-localization and mapping for autonomous systems like drones, but it is incremental as it builds on existing LiDAR-based optimization methods with a focus on efficient map representation.
The paper tackles LiDAR-only odometry and mapping by proposing LiODOM, which minimizes a loss function from weighted point-to-line correspondences with a local map, achieving favorable performance compared to other solutions on public datasets and on-board an aerial platform.
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.