TY - GEN
T1 - Incremental probabilistic geometry estimation for robot scene understanding
AU - Cahier, Louis Kenzo
AU - Ogata, Tetsuya
AU - Okuno, Hiroshi G.
PY - 2012
Y1 - 2012
N2 - Our goal is to give mobile robots a rich representation of their environment as fast as possible. Current mapping methods such as SLAM are often sparse, and scene reconstruction methods using tilting laser scanners are relatively slow. In this paper, we outline a new method for iterative construction of a geometric mesh using streaming time-of-flight range data. Our results show that our algorithm can produce a stable representation after 6 frames, with higher accuracy than raw time-of-flight data.
AB - Our goal is to give mobile robots a rich representation of their environment as fast as possible. Current mapping methods such as SLAM are often sparse, and scene reconstruction methods using tilting laser scanners are relatively slow. In this paper, we outline a new method for iterative construction of a geometric mesh using streaming time-of-flight range data. Our results show that our algorithm can produce a stable representation after 6 frames, with higher accuracy than raw time-of-flight data.
UR - http://www.scopus.com/inward/record.url?scp=84864449345&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84864449345&partnerID=8YFLogxK
U2 - 10.1109/ICRA.2012.6225343
DO - 10.1109/ICRA.2012.6225343
M3 - Conference contribution
AN - SCOPUS:84864449345
SN - 9781467314039
T3 - Proceedings - IEEE International Conference on Robotics and Automation
SP - 3625
EP - 3630
BT - 2012 IEEE International Conference on Robotics and Automation, ICRA 2012
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2012 IEEE International Conference on Robotics and Automation, ICRA 2012
Y2 - 14 May 2012 through 18 May 2012
ER -