TY - GEN
T1 - Probabilistic shadow information spaces
AU - Yu, Jingjin
AU - LaValle, Steven M.
PY - 2010
Y1 - 2010
N2 - This paper introduces a Bayesian filter that is specifically designed for counting targets that move outside of the field of view while performing a sensor sweep. Information space concepts are used to dramatically reduce the filter complexity so that information is processed only when the shadow region (all points invisible to the sensors) changes combinatorially or targets pass in and out of view. Previous work assumed perfect observations; however, this paper extends the approach to enable probabilistic disturbances. Practical algorithms are introduced, implemented, and demonstrated for computing the filter outputs based on realistic data.
AB - This paper introduces a Bayesian filter that is specifically designed for counting targets that move outside of the field of view while performing a sensor sweep. Information space concepts are used to dramatically reduce the filter complexity so that information is processed only when the shadow region (all points invisible to the sensors) changes combinatorially or targets pass in and out of view. Previous work assumed perfect observations; however, this paper extends the approach to enable probabilistic disturbances. Practical algorithms are introduced, implemented, and demonstrated for computing the filter outputs based on realistic data.
UR - http://www.scopus.com/inward/record.url?scp=77955817007&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=77955817007&partnerID=8YFLogxK
U2 - 10.1109/ROBOT.2010.5509588
DO - 10.1109/ROBOT.2010.5509588
M3 - Conference contribution
AN - SCOPUS:77955817007
SN - 9781424450381
T3 - Proceedings - IEEE International Conference on Robotics and Automation
SP - 3543
EP - 3549
BT - 2010 IEEE International Conference on Robotics and Automation, ICRA 2010
T2 - 2010 IEEE International Conference on Robotics and Automation, ICRA 2010
Y2 - 3 May 2010 through 7 May 2010
ER -