TY - JOUR
T1 - Bayesian estimation and Kalman filtering
T2 - a unified framework for mobile robot localization
AU - Roumeliotis, Stergios I.
AU - Bekey, George A.
PY - 2000/12/3
Y1 - 2000/12/3
N2 - Decision and estimation theory are closely related topics in applied probability. In this paper, Bayesian hypothesis testing is combined with Kalman filtering to merge two different approaches to map-based mobile robot localization; namely Markov localization and pose tracking. A robot carries proprioceptive sensors that monitor its motion and allow it to estimate its trajectory as it moves away from a known location. A single Kalman filter is used for tracking the pose displacements of the robot inbetween different areas. The robot is also equipped with exteroceptive sensors that seek for landmarks in the environment. Simple feature extraction algorithms process the incoming signals and suggest potential corresponding locations on the map. Bayesian hypothesis testing is applied in order to combine the continuous Kalman filter displacement estimates with the discrete landmark pose measurement events. Within this framework, also known as Multiple Hypothesis Tracking, multi-modal probability distribution functions can be represented and this inherent limitation of the Kalman filter is overcome.
AB - Decision and estimation theory are closely related topics in applied probability. In this paper, Bayesian hypothesis testing is combined with Kalman filtering to merge two different approaches to map-based mobile robot localization; namely Markov localization and pose tracking. A robot carries proprioceptive sensors that monitor its motion and allow it to estimate its trajectory as it moves away from a known location. A single Kalman filter is used for tracking the pose displacements of the robot inbetween different areas. The robot is also equipped with exteroceptive sensors that seek for landmarks in the environment. Simple feature extraction algorithms process the incoming signals and suggest potential corresponding locations on the map. Bayesian hypothesis testing is applied in order to combine the continuous Kalman filter displacement estimates with the discrete landmark pose measurement events. Within this framework, also known as Multiple Hypothesis Tracking, multi-modal probability distribution functions can be represented and this inherent limitation of the Kalman filter is overcome.
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M3 - Article
AN - SCOPUS:0033718307
SN - 1050-4729
VL - 3
SP - 2985
EP - 2992
JO - Proceedings - IEEE International Conference on Robotics and Automation
JF - Proceedings - IEEE International Conference on Robotics and Automation
ER -