BIM-SLAM: Integrating BIM Models in Multi-session SLAM for Lifelong Mapping using 3D LiDAR

TL;DR

This paper presents a methodology that integrates BIM models with multi-session 3D LiDAR SLAM to improve indoor mapping accuracy, reduce drift, and facilitate visualization without prior pose knowledge.

Contribution

It introduces a novel framework combining BIM models with multi-session SLAM for lifelong indoor mapping and dynamic element reconstruction.

Findings

Enhanced map accuracy and alignment with BIM models.

Effective identification and reconstruction of new elements.

Reduced drift in multi-session SLAM.

Abstract

While 3D LiDAR sensor technology is becoming more advanced and cheaper every day, the growth of digitalization in the AEC industry contributes to the fact that 3D building information models (BIM models) are now available for a large part of the built environment. These two facts open the question of how 3D models can support 3D LiDAR long-term SLAM in indoor, GPS-denied environments. This paper proposes a methodology that leverages BIM models to create an updated map of indoor environments with sequential LiDAR measurements. Session data (pose graph-based map and descriptors) are initially generated from BIM models. Then, real-world data is aligned with the session data from the model using multi-session anchoring while minimizing the drift on the real-world data. Finally, the new elements not present in the BIM model are identified, grouped, and reconstructed in a surface representation, allowing a better visualization next to the BIM model. The framework enables the creation of a coherent map aligned with the BIM model that does not require prior knowledge of the initial pose of the robot, and it does not need to be inside the map.