Towards Automated Void Detection for Search and Rescue with 3D Perception

In a structural collapse, debris piles up in a chaotic and unstable manner, creating pockets and void spaces that are difficult to see or access. Often, these regions have the highest chances of concealing survivors and identifying such regions can increase the success of a search and rescue (SAR) operation while ensuring the safety of both survivors and rescue teams. In this paper, we present an approach for ex post facto void detection in rubble piles by using registered 3D point clouds reconstructed from aerial images captured at multiple times on the scene. We perform a temporal layering of these point clouds to capture the dynamic surface of the rubble pile from multiple days of the SAR operation and analyze this 3D structure to detect candidate regions corresponding to void spaces. The layering is achieved by a parallel 3D point cloud reconstruction of the scene using the COLMAP Structure from Motion pipeline. The void detection is achieved by applying multiple point filtering criteria in thin segments of the 3D point clouds of the rubble. We test our approach on aerial images collected from the Surfside Structural Collapse at Miami in June 2021. Our method achieves an improvement in registration compared to the use of standard point cloud registration methods on individual 3D reconstructions. Through our method, we see translation errors reduce by 82%. Additionally, our method detects 9 out of 10 void spaces that were observed by experts in the rubble.