DocumentCode :
2576182
Title :
Simultaneous localization and mapping in dense environments
Author :
Chakravorty, S. ; Saha, R.
Author_Institution :
Dept. of Aerosp. Eng., Texas A&M Univ., College Station, TX, USA
fYear :
2009
fDate :
11-14 Oct. 2009
Firstpage :
2769
Lastpage :
2774
Abstract :
A hybrid Bayesian/ frequentist approach is presented for the simultaneous localization and mapping problem (SLAM). A frequentist approach is proposed for mapping with time varying robotic poses and is generalized to the case when the robotic pose is both time varying and uncertain. The SLAM problem is then solved in two steps: 1) the robot is localized with respect to a sparse set of landmarks in the map using a Bayes filter and a belief on the robot pose is formed, and 2) this belief on the robot pose is used to map the rest of the map using the frequentist estimator. The hybrid methodology is shown to have complexity linear in the map components, is robust to the data association problem and is provably consistent.
Keywords :
Bayes methods; SLAM (robots); filtering theory; mobile robots; pose estimation; robot vision; time-varying systems; Bayes filter; Bayesian/ frequentist approach; SLAM; dense environment; simultaneous localization-and-mapping; time varying robotic pose mapping; Aerospace engineering; Bayesian methods; Information filtering; Information filters; Kalman filters; Optimization methods; Robots; Robustness; Simultaneous localization and mapping; Trajectory;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Systems, Man and Cybernetics, 2009. SMC 2009. IEEE International Conference on
Conference_Location :
San Antonio, TX
ISSN :
1062-922X
Print_ISBN :
978-1-4244-2793-2
Electronic_ISBN :
1062-922X
Type :
conf
DOI :
10.1109/ICSMC.2009.5346573
Filename :
5346573
Link To Document :
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