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A comparison of maximum likelihood methods for appearance-based minimalistic SLAM

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4 Author(s)
Rybski, P.E. ; Dept. of Comput. Sci. & Eng., Minnesota Univ., Minneapolis, MN, USA ; Roumeliotis, S.I. ; Gini, M. ; Papanikolopoulos, N.

This paper compares the performances of several algorithms that address the problem of Simultaneous Localization and Mapping (SLAM) for the case of very small, resource-limited robots. These robots have poor odometry and can typically only carry a single monocular camera. These algorithms do not make the typical SLAM assumption that metric distance/bearing information to landmarks is available. Instead, the robot registers a distinctive sensor "signature", based on its current location, which is used to match robot positions. The performances of a physics-inspired maximum likelihood (ML) estimator, the iterated form of the Extended Kalman Filter (IEKF), and a batch-processed linearized ML estimator are compared under various odometric noise models.

Published in:

Robotics and Automation, 2004. Proceedings. ICRA '04. 2004 IEEE International Conference on  (Volume:2 )

Date of Conference:

April 26-May 1, 2004

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