BIMCaP: BIM-based AI-supported LiDAR-Camera Pose Refinement
It improves accuracy and cost-effectiveness for 3D mapping in fields like construction and emergency response, though it is incremental as it builds on existing bundle adjustment techniques.
This paper tackles the problem of indoor mapping by integrating mobile LiDAR and camera data with building information models (BIMs) to refine sensor poses, achieving a reduction in translational error by over 4 cm compared to state-of-the-art methods.
This paper introduces BIMCaP, a novel method to integrate mobile 3D sparse LiDAR data and camera measurements with pre-existing building information models (BIMs), enhancing fast and accurate indoor mapping with affordable sensors. BIMCaP refines sensor poses by leveraging a 3D BIM and employing a bundle adjustment technique to align real-world measurements with the model. Experiments using real-world open-access data show that BIMCaP achieves superior accuracy, reducing translational error by over 4 cm compared to current state-of-the-art methods. This advancement enhances the accuracy and cost-effectiveness of 3D mapping methodologies like SLAM. BIMCaP's improvements benefit various fields, including construction site management and emergency response, by providing up-to-date, aligned digital maps for better decision-making and productivity. Link to the repository: https://github.com/MigVega/BIMCaP