NDLI: On stochastically observable directions of the estimation theoretic SLAM state space

Content Provider	IEEE Xplore Digital Library
Author	Perera, L.D.L. Nettleton, E.
Copyright Year	2010
Description	Author affiliation: Australian Centre for Field Robotics, The University of Sydney, NSW 2006, Australia (Perera, L.D.L.; Nettleton, E.)
Abstract	The theory of stochastic observability is vital in describing the performance of Simultaneous Localization and Mapping (SLAM) as a nonlinear stochastic state estimation problem quantifying effects of random noise on its observability. We show that the eigen space corresponding to the stochastically unobservable states of the state error covariance matrix of the SLAM problem initialized with unknown initial conditions are in the null space of the information matrix associated with observations of the SLAM problem. We establish by using theoretical analysis and examples that the stochastically unobservable directions of the SLAM state space can be changed by modifying the observation model of the SLAM problem. We then use simulations and experiments to show that stochastically observable directions of state space and their degree of stochastic observability can be modified as required in a particular application (such as surveying, mapping and surveillance) by changing the vehicle path with respect to the landmarks in the environment by selecting which landmarks to observe and by modifying the observation model.
Starting Page	4324
Ending Page	4331
File Size	1128136
Page Count	8
File Format	PDF
ISBN	9781424466740
ISSN	21530866
e-ISBN	9781424466764
DOI	10.1109/IROS.2010.5653623
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-10-18
Publisher Place	Taiwan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Simultaneous localization and mapping Observability Covariance matrix Vehicles Stochastic processes Noise Jacobian matrices stochastic observability SLAM
Content Type	Text
Resource Type	Article

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