  This paper presents a localization system for mobile robots enabling precise
localization in inaccurate building models. The approach leverages local
referencing to counteract inherent deviations between as-planned and as-built
data for locally accurate registration. We further fuse a novel image-based
robust outlier detector with LiDAR data to reject a wide range of outlier
measurements from clutter, dynamic objects, and sensor failures. We evaluate
the proposed approach on a mobile robot in a challenging real world building
construction site. It consistently outperforms the traditional ICP-based
alingment, reducing localization error by at least 30%.
