A Comparison of Point Cloud Registration Techniques for on-site Disaster Data from the Surfside Structural Collapse

Ananya Ball, Robert Ladig, Pranav Goyal, John Galeotti, Howie Choset, David Merrick, Robin Murphy

Publikation: Kapitel i bog/rapport/konference-proceedingKonferencebidrag i proceedingsForskningpeer review

Abstract

3D representations of geographical surfaces in the form of dense point clouds can be a valuable tool for documenting and reconstructing a structural collapse, such as the 2021 Champlain Towers Condominium collapse in Surfside, Florida. Point cloud data reconstructed from aerial footage taken by uncrewed aerial systems at frequent intervals from a dynamic search and rescue scene poses significant challenges. Properly aligning large point clouds in this context, or registering them, poses noteworthy issues as they capture multiple regions whose geometries change over time. These regions denote dynamic features such as excavation machinery, cones marking boundaries and the structural collapse rubble itself. In this paper, the performances of commonly used point cloud registration methods for dynamic scenes present in the raw data are studied. The use of Iterative Closest Point (ICP), Rigid - Coherent Point Drift (CPD) and PointNetLK for registering dense point clouds, reconstructed sequentially over a time-frame of five days, is studied and evaluated. All methods are compared by error in performance, execution time, and robustness with a concluding analysis and a judgement of the preeminent method for the specific data at hand is provided.

OriginalsprogEngelsk
Titel2022 - IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR)
ForlagIEEE
Publikationsdato2022
Sider244-250
ISBN (Elektronisk)9781665456807
DOI
StatusUdgivet - 2022
Udgivet eksterntJa
Begivenhed2022 IEEE International Symposium on Safety, Security, and Rescue Robotics, SSRR 2022 - Sevilla, Spanien
Varighed: 8. nov. 202210. nov. 2022

Konference

Konference2022 IEEE International Symposium on Safety, Security, and Rescue Robotics, SSRR 2022
Land/OmrådeSpanien
BySevilla
Periode08/11/202210/11/2022
NavnIEEE International Symposium on Safety, Security and Rescue Robotics
ISSN2374-3247

Bibliografisk note

Publisher Copyright:
© 2022 IEEE.

Fingeraftryk

Dyk ned i forskningsemnerne om 'A Comparison of Point Cloud Registration Techniques for on-site Disaster Data from the Surfside Structural Collapse'. Sammen danner de et unikt fingeraftryk.

Citationsformater