BIM-Loc: BIM-Integrated Discrepancy-Aware LiDAR-based Indoor Localization

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.

24 pages, 21 figures, accepted by International Journal of Robotics Research (IJRR), to be published

References in corpus (11)

• Past, Present, and Future of Simultaneous Localization And Mapping: Towards the Robust-Perception Age
• KISS-ICP: In Defense of Point-to-Point ICP -- Simple, Accurate, and Robust Registration If Done the Right Way
• Semantic localization on BIM-generated maps using a 3D LiDAR sensor
• GLIM: 3D Range-Inertial Localization and Mapping with GPU-Accelerated Scan Matching Factors
• Robust Lifelong Indoor LiDAR Localization using the Area Graph
• NV-LIO: LiDAR-Inertial Odometry using Normal Vectors Towards Robust SLAM in Multifloor Environments
• PALoc: Advancing SLAM Benchmarking with Prior-Assisted 6-DoF Trajectory Generation and Uncertainty Estimation
• SLAM2REF: Advancing Long-Term Mapping with 3D LiDAR and Reference Map Integration for Precise 6-DoF Trajectory Estimation and Map Extension
• FP-Loc: Lightweight and Drift-free Floor Plan-assisted LiDAR Localization
• Occupancy Grid Map to Pose Graph-based Map: Robust BIM-based 2D-LiDAR Localization for Lifelong Indoor Navigation in Changing and Dynamic Environments
• MICP-L: Mesh-based ICP for Robot Localization using Hardware-Accelerated Ray Casting