detalle del documento
IDENTIFICACIÓN
oai:arXiv.org:2410.05433
Tema
Computer Science - RoboticsAutor
Gentil, Cedric Le Falque, Raphael Vidal-Calleja, TeresaCategoría
Computer Science
Año
2024
fecha de cotización
16/10/2024
Palabras clave
registration map 2fast-2lamaa lidarResumen
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
Systematic Feature Design for Cycle Life Prediction of Lithium-Ion Batteries During Formation
cycle prediction framework feature design features life formation
