Paper Info
Title
Markov-Kalman localization for mobile robots
Abstract
Localization is one of the fundamental problems in mobile robot navigation. Recent experiments have shown that, in general, grid-based Markov localization is more robust than Kalman filtering, while the latter can be more accurate than the former In this paper, we present a novel approach called Markov-Kalman localization (ML-EKF) which is a combination of both methods. ML-EKF is well suited for robots observing known landmarks, having a rough estimate of their movements, and which might be displaced to arbitrary positions at any time. Experimental results show that our method outperforms both of its underlying techniques by inheriting the accuracy of Kalman filtering and the robustness and relocalization speed of the Markov method.
Year
DOI
Venue
2002
10.1109/ICPR.2002.1048374
Pattern Recognition, 2002. Proceedings. 16th International Conference  
Keywords
Field
DocType
Kalman filters,Markov processes,image motion analysis,mobile robots,navigation,Kalman filtering,Markov-Kalman localization,accuracy,extended Kalman filter,grid-based Markov localization,known landmarks,mobile robot navigation,motion models,relocalization speed,robustness
Computer vision,Markov process,Fast Kalman filter,Computer science,Markov chain,Robustness (computer science),Kalman filter,Artificial intelligence,Mobile robot navigation,Robot,Mobile robot
Conference
Volume
ISSN
Citations 
2
1051-4651
14
PageRank 
References 
Authors
1.68
9
1
Name
Order
Citations
PageRank
Jens-Steffen Gutmann165776.64