Precise Robot Localization in Architectural 3D Plans
This addresses precise localization for mobile robots in challenging environments like construction sites, but it is incremental as it builds on existing sensor fusion techniques.
The paper tackles robot localization in inaccurate building models by fusing LiDAR with an image-based outlier detector, achieving at least a 30% reduction in error compared to traditional methods on a real construction site.
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%.