AEROS: AdaptivE RObust Least-Squares for Graph-Based SLAM.

ABSTRACT: In robot localisation and mapping, outliers are unavoidable when loop-closure measurements are taken into account. A single false-positive loop-closure can have a very negative impact on SLAM problems causing an inferior trajectory to be produced or even for the optimisation to fail entirely. To address this issue, popular existing approaches define a hard switch for each loop-closure constraint. This paper presents AEROS, a novel approach to adaptively solve a robust least-squares minimisation problem by adding just a single extra latent parameter. It can be used in the back-end component of the SLAM system to enable generalised robust cost minimisation by simultaneously estimating the continuous latent parameter along with the set of sensor poses in a single joint optimisation. This lead

SUBMITTER: Ramezani M 

PROVIDER: S-EPMC9010720 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature