Document detail
ID
oai:arXiv.org:2410.05433
Topic
Computer Science - RoboticsAuthor
Gentil, Cedric Le Falque, Raphael Vidal-Calleja, TeresaCategory
Computer Science
Year
2024
listing date
10/16/2024
Keywords
registration map 2fast-2lamaa lidarAbstract
This document presents a framework for lidar-inertial localisation and mapping named 2Fast-2Lamaa.
The method revolves around two main steps which are the inertial-aided undistortion of the lidar data and the scan-to-map registration using a distance-field representation of the environment.
The initialisation-free undistortion uses inertial data to constrain the continuous trajectory of the sensor during the lidar scan.
The eleven DoFs that fully characterise the trajectory are estimated by minimising lidar point-to-line and point-to-plane distances in a non-linear least-square formulation.
The registration uses a map that provides a distance field for the environment based on Gaussian Process regression.
The pose of an undistorted lidar scan is optimised to minimise the distance field queries of its points with respect to the map.
After registration, the new geometric information is efficiently integrated into the map.
The soundness of 2Fast-2Lamaa is demonstrated over several datasets (qualitative evaluation only).
The real-time implementation is made publicly available at https://github.com/UTS-RI/2fast2lamaa.
Gentil, Cedric Le,Falque, Raphael,Vidal-Calleja, Teresa, 2024, 2FAST-2LAMAA: A Lidar-Inertial Localisation and Mapping Framework for Non-Static Environments
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