AEROS: Adaptive RObust least-Squares for Graph-Based SLAM

TL;DR

AEROS introduces an adaptive robust least-squares method for graph-based SLAM that jointly estimates sensor poses and a latent outlier parameter, improving robustness against false loop-closures.

Contribution

It proposes a novel adaptive robust optimization approach with a single latent parameter, compatible with standard Gaussian solvers, for improved SLAM outlier handling.

Findings

Outperforms state-of-the-art methods on synthetic datasets

Shows superior robustness on real LiDAR-SLAM data

Compatible with incremental estimation techniques like iSAM

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 problem 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 leads to a very closely curve fitting on the distribution of the residuals, thereby reducing the effect of outliers. Additionally, we formulate the robust optimisation problem using standard Gaussian factors so that it can be solved by direct application of popular incremental estimation approaches such as iSAM. Experimental results on publicly available synthetic datasets and real LiDAR-SLAM datasets collected from the 2D and 3D LiDAR systems show the competitiveness of our approach with the state-of-the-art techniques and its superiority on real world scenarios.