Title: Human-in-the-Loop SLAM,
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.