Interior structural change detection using a 3D model and LiDAR segmentation

Abstract

Detecting changes of indoor environments with respect to a 3D model is important for building monitoring and management. Existing change detection methods based on LiDAR segmentation and comparison with 3D models are limited to simple environments without temporary changes or moving objects. The aim of this paper is to propose a novel change detection method based on LiDAR segmentation for complex environments. We formulate the problem of detecting differences between the 3D model and the real environment as the detection of differences between real LiDAR scans captured in the environment and synthetic LiDAR scans generated in the 3D model. This allows real-time building change detection and classification using a mobile LiDAR. A two-branch convolutional network is proposed to detect differences between the 3D model and LiDAR scans. Synthetic LiDAR scans are generated in the 3D model using the estimated poses of a set of real LiDAR scans. The network is trained with pairs of synthetic and real LiDAR scans and tested with new real LiDAR scans. Each point of real LiDAR scans is classified into one of four categories: unchanged, structural change, moving objects and temporary change. A comparison is performed between the performance of four backbone architectures to find a suitable backbone architecture for change detection networks. Experimental results show that the proposed approach can achieve 94% overall change classification accuracy with the SqueezeNet-based change detection network and the trained network is transferable to comparable indoor environments. This research enables updating 3D models of complex indoor environments efficiently using a mobile LiDAR scanner.

Topics & Concepts