Problem
Research questions and friction points this paper is trying to address.
indoor localization
feature-sparse environments
Building Information Model (BIM)
LiDAR-based localization
map availability
BIM-Loc: BIM-Integrated Discrepancy-Aware LiDAR-based Indoor Localization
🤖 AI Summary
This work addresses the challenges of inaccurate and non-robust localization faced by service and inspection robots in feature-sparse indoor environments due to the absence of salient landmarks. To overcome this, we propose a LiDAR-based localization method that integrates Building Information Modeling (BIM). Our approach employs multi-hit ray casting for efficient data association and constructs a BIM-integrated pose graph optimization framework to enforce structural consistency constraints. Furthermore, a hierarchical Bayesian inference module is designed to model continuous surface discrepancies between the real environment and the BIM. Coordinate alignment is achieved through 2D texture projection combined with online BIM–point cloud registration. Experimental results demonstrate that our method significantly outperforms existing map-based localization approaches in both simulated and real-world scenarios, achieving notable improvements in accuracy and robustness.
📝 Abstract
Accurate and robust localization is a fundamental requirement for service and inspection robots, particularly in feature-sparse indoor environments where traditional systems struggle due to a lack of distinct landmarks. While prior maps can enhance robustness, precise and compact maps capturing real-world details are often unavailable for new or frequently changing environments. This paper presents BIM-Loc, a novel discrepancy-aware LiDAR-based localization method that directly integrates Building Information Models (BIM) from the design phase. BIM-Loc simultaneously estimates trajectories aligned with the BIM coordinate system and identifies discrepancies between real-world observations and the as-designed BIM in an online fashion. Our core contributions include: (1) a novel multi-hit ray casting strategy for efficient BIM-point data association and projection of 3D observations into 2D texture space; (2) a pose graph optimization framework with BIM-integrated factors that enforces consistency among odometry, sequential scans, and BIM structures; and (3) a hierarchical Bayesian inference module that incrementally updates a continuous 2D surface representation for discrepancy detection, propagating updates from the pixel to the structure level. Extensive evaluations in both simulation and real-world applications demonstrate that BIM-Loc significantly outperforms state-of-the-art map-based methods in localization accuracy and robustness.
Innovation
Methods, ideas, or system contributions that make the work stand out.
BIM-integrated localization
discrepancy-aware
multi-hit ray casting
pose graph optimization
hierarchical Bayesian inference
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