Ultrasound scanning is an efficient imaging modality preferred for quick medical procedures. However, due to the lack of skilled sonographers, researchers have developed many Robotic Ultrasound System (RUS) prototypes for various procedures. Most of these systems have a human-in-the-loop and require an expert to point the robot to the region of the subject to be scanned. Only a few systems try to incorporate some knowledge from the exterior shape of the subject for ultrasound scanning. Accurate 3D surface reconstruction of a patient's exterior can enable an RUS to perceive subjects more like a clinician would. It can help localize the subject for the robot while eliminating input from an expert. Ultrasound scanning trajectories can be better planned if the RUS first detects critical regions on the surface of the subject and corresponding curvatures. We use an RGB-D sensor to acquire point clouds representing the 3D surface of the subject, which in the present work is for a lower-torso leg phantom. A consolidated pipeline for creating an optimized 3D surface reconstruction of a subject is presented and is used to autonomously identify a region of interest for scanning femoral vessels with an ultrasound probe. To make our system more robust to inter-subject variations in shape and size, we incorporate a trajectory optimization module of the RUS-mounted RGB-D sensor. To this end, we introduce a comprehensive evaluation score to quantify the quality of point cloud reconstructions. The resulting improvements in 3D surface scanning and reconstruction enable near-automation in generating ultrasound scanning trajectories for femoral vessels. Our pipeline produces ultrasound images with an average ZNCC score of 0.86 and our 3D point cloud reconstructions are accurate up to le-5 m from a ground-truth high-resolution CT scan.

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.