TY - GEN
T1 - Building large-scale occupancy maps using an infinite mixture of Gaussian process experts
AU - Kim, Soohwan
AU - Kim, Jonghyuk
PY - 2012
Y1 - 2012
N2 - This paper proposes a novel method of occupancy map building for large-scale applications. Although Gaussian processes have been successfully applied to occupancy map building, it suffers from high computational complexity of O(n3), where n is the number of training data, limiting its use for large-scale mappings. We propose to take a divide-and-conquer approach by partitioning training data into manageable subsets by combining a Dirichlet process mixture on top of a Gaussian process, which turns into an infinite mixtures of Gaussian process experts. Experimental results with simulated data show that our method produces accurate occupancy maps while maintaining the scalability.
AB - This paper proposes a novel method of occupancy map building for large-scale applications. Although Gaussian processes have been successfully applied to occupancy map building, it suffers from high computational complexity of O(n3), where n is the number of training data, limiting its use for large-scale mappings. We propose to take a divide-and-conquer approach by partitioning training data into manageable subsets by combining a Dirichlet process mixture on top of a Gaussian process, which turns into an infinite mixtures of Gaussian process experts. Experimental results with simulated data show that our method produces accurate occupancy maps while maintaining the scalability.
UR - http://www.scopus.com/inward/record.url?scp=84879954877&partnerID=8YFLogxK
M3 - Conference contribution
SN - 9780980740431
T3 - Australasian Conference on Robotics and Automation, ACRA
BT - Proceedings of the 2012 Australasian Conference on Robotics and Automation, ACRA 2012
T2 - 2012 Australasian Conference on Robotics and Automation, ACRA 2012
Y2 - 3 December 2012 through 5 December 2012
ER -