TY - GEN
T1 - A Comparison of Point Cloud Registration Techniques for on-site Disaster Data from the Surfside Structural Collapse
AU - Ball, Ananya
AU - Ladig, Robert
AU - Goyal, Pranav
AU - Galeotti, John
AU - Choset, Howie
AU - Merrick, David
AU - Murphy, Robin
N1 - Funding Information:
*Portions of this work were supported by NSF Award CMMI 2140528. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation. 1A. Bal J. Galeotti and H. Choset are with the Robotics Institute, Carnegie Mellon University, Pittsburgh, PA, USA [email protected] 2R. Ladig is with the Department of Science and Engineering, Rit-sumeikan University, Kusatsu, JAPAN 3P. Goyal is with the Birla Institute of Technology & Science - Pilani, Goa, INDIA 4D. Merrick is with the College of Social Science and Public Policy, Florida State University, FL, USA 5R. Murphy is Raytheon Professor of Computer Science and Engineering, Texas A&M University, TX, USA
Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
U2 - 10.1109/SSRR56537.2022.10018779
DO - 10.1109/SSRR56537.2022.10018779
M3 - Article in proceedings
AN - SCOPUS:85147550091
T3 - IEEE International Symposium on Safety, Security and Rescue Robotics
SP - 244
EP - 250
BT - 2022 - IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR)
PB - IEEE
T2 - 2022 IEEE International Symposium on Safety, Security, and Rescue Robotics, SSRR 2022
Y2 - 8 November 2022 through 10 November 2022
ER -