DocumentCode :
250153
Title :
Modern MAP inference methods for accurate and fast occupancy grid mapping on higher order factor graphs
Author :
Dhiman, Vikas ; Kundu, A. ; Dellaert, Frank ; Corso, Jason J.
Author_Institution :
Dept. of Comput. Sci. & Eng., SUNY at Buffalo, Buffalo, NY, USA
fYear :
2014
fDate :
May 31 2014-June 7 2014
Firstpage :
2037
Lastpage :
2044
Abstract :
Using the inverse sensor model has been popular in occupancy grid mapping. However, it is widely known that applying the inverse sensor model to mapping requires certain assumptions that are not necessarily true. Even the works that use forward sensor models have relied on methods like expectation maximization or Gibbs sampling which have been succeeded by more effective methods of maximum a posteriori (MAP) inference over graphical models. In this paper, we propose the use of modern MAP inference methods along with the forward sensor model. Our implementation and experimental results demonstrate that these modern inference methods deliver more accurate maps more efficiently than previously used methods.
Keywords :
control engineering computing; expectation-maximisation algorithm; graph theory; inference mechanisms; mobile robots; Gibbs sampling; expectation maximization; forward sensor model; graphical model; higher order factor graphs; inference methods; inverse sensor model; maximum a posteriori inference; modern MAP inference method; occupancy grid mapping; Belief propagation; Collision avoidance; Computational modeling; Inference algorithms; Measurement by laser beam; Robot sensing systems;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Robotics and Automation (ICRA), 2014 IEEE International Conference on
Conference_Location :
Hong Kong
Type :
conf
DOI :
10.1109/ICRA.2014.6907129
Filename :
6907129
Link To Document :
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