TY - GEN
T1 - Fusing concurrent orthogonal wide-aperture sonar images for dense underwater 3D reconstruction
AU - McConnell, John
AU - Martin, John D.
AU - Englot, Brendan
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/10/24
Y1 - 2020/10/24
N2 - We propose a novel approach to handling the ambiguity in elevation angle associated with the observations of a forward looking multi-beam imaging sonar, and the challenges it poses for performing an accurate 3D reconstruction. We utilize a pair of sonars with orthogonal axes of uncertainty to independently observe the same points in the environment from two different perspectives, and associate these observations. Using these concurrent observations, we can create a dense, fully defined point cloud at every time-step to aid in reconstructing the 3D geometry of underwater scenes. We will evaluate our method in the context of the current state of the art, for which strong assumptions on object geometry limit applicability to generalized 3D scenes. We will discuss results from laboratory tests that quantitatively benchmark our algorithm's reconstruction capabilities, and results from a real-world, tidal river basin which qualitatively demonstrate our ability to reconstruct a cluttered field of underwater objects.
AB - We propose a novel approach to handling the ambiguity in elevation angle associated with the observations of a forward looking multi-beam imaging sonar, and the challenges it poses for performing an accurate 3D reconstruction. We utilize a pair of sonars with orthogonal axes of uncertainty to independently observe the same points in the environment from two different perspectives, and associate these observations. Using these concurrent observations, we can create a dense, fully defined point cloud at every time-step to aid in reconstructing the 3D geometry of underwater scenes. We will evaluate our method in the context of the current state of the art, for which strong assumptions on object geometry limit applicability to generalized 3D scenes. We will discuss results from laboratory tests that quantitatively benchmark our algorithm's reconstruction capabilities, and results from a real-world, tidal river basin which qualitatively demonstrate our ability to reconstruct a cluttered field of underwater objects.
UR - http://www.scopus.com/inward/record.url?scp=85102408329&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85102408329&partnerID=8YFLogxK
U2 - 10.1109/IROS45743.2020.9340995
DO - 10.1109/IROS45743.2020.9340995
M3 - Conference contribution
AN - SCOPUS:85102408329
T3 - IEEE International Conference on Intelligent Robots and Systems
SP - 1653
EP - 1660
BT - 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2020
T2 - 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2020
Y2 - 24 October 2020 through 24 January 2021
ER -