TY - GEN
T1 - Bayesian generalized kernel inference for occupancy map prediction
AU - Doherty, Kevin
AU - Wang, Jinkun
AU - Englot, Brendan
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/7/21
Y1 - 2017/7/21
N2 - We consider the problem of building accurate and descriptive 3D occupancy maps of an environment from sparse and noisy range sensor data. We seek to accomplish this task by constructing a predictive model online and inferring the occupancy probability of regions we have not directly observed. We propose a novel algorithm leveraging recent advances in data structures for mapping, sparse kernels, and Bayesian nonparametric inference. The resulting inference model has several desirable properties in comparison to existing methods, including speed of computation, the ability to be recursively updated without approximation, and consistency between batch and online inference. The method also reverts to the use of a specified prior state when insufficient relevant training data exist to predict the occupancy probability of a query point, a property which is attractive for motion planning and exploration applications with mobile robots.
AB - We consider the problem of building accurate and descriptive 3D occupancy maps of an environment from sparse and noisy range sensor data. We seek to accomplish this task by constructing a predictive model online and inferring the occupancy probability of regions we have not directly observed. We propose a novel algorithm leveraging recent advances in data structures for mapping, sparse kernels, and Bayesian nonparametric inference. The resulting inference model has several desirable properties in comparison to existing methods, including speed of computation, the ability to be recursively updated without approximation, and consistency between batch and online inference. The method also reverts to the use of a specified prior state when insufficient relevant training data exist to predict the occupancy probability of a query point, a property which is attractive for motion planning and exploration applications with mobile robots.
UR - http://www.scopus.com/inward/record.url?scp=85027979905&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85027979905&partnerID=8YFLogxK
U2 - 10.1109/ICRA.2017.7989356
DO - 10.1109/ICRA.2017.7989356
M3 - Conference contribution
AN - SCOPUS:85027979905
T3 - Proceedings - IEEE International Conference on Robotics and Automation
SP - 3118
EP - 3124
BT - ICRA 2017 - IEEE International Conference on Robotics and Automation
T2 - 2017 IEEE International Conference on Robotics and Automation, ICRA 2017
Y2 - 29 May 2017 through 3 June 2017
ER -