Human-in-the-Loop SLAM

TL;DR

HitL-SLAM introduces a systematic method for incorporating sparse human corrections into large-scale SLAM, improving map consistency even with poor initial estimates by jointly inferring and optimizing human inputs.

Contribution

The paper presents a novel EM-based framework and correction factor extensions for pose graph SLAM that effectively integrate human corrections into the mapping process.

Findings

Successfully generates globally consistent maps from poor initial estimates.

Effectively infers human corrections even when inputs are erroneous or rank-deficient.

Enhances large-scale SLAM robustness with human-in-the-loop optimization.

Abstract

Building large-scale, globally consistent maps is a challenging problem, made more difficult in environments with limited access, sparse features, or when using data collected by novice users. For such scenarios, where state-of-the-art mapping algorithms produce globally inconsistent maps, we introduce a systematic approach to incorporating sparse human corrections, which we term Human-in-the-Loop Simultaneous Localization and Mapping (HitL-SLAM). Given an initial factor graph for pose graph SLAM, HitL-SLAM accepts approximate, potentially erroneous, and rank-deficient human input, infers the intended correction via expectation maximization (EM), back-propagates the extracted corrections over the pose graph, and finally jointly optimizes the factor graph including the human inputs as human correction factor terms, to yield globally consistent large-scale maps. We thus contribute an EM formulation for inferring potentially rank-deficient human corrections to mapping, and human correction factor extensions to the factor graphs for pose graph SLAM that result in a principled approach to joint optimization of the pose graph while simultaneously accounting for multiple forms of human correction. We present empirical results showing the effectiveness of HitL-SLAM at generating globally accurate and consistent maps even when given poor initial estimates of the map.