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Robustness of the Quadratic Antiparticle Filter forRobot Localization
KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS. KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP. (CAS/CVAP/CSC)ORCID iD: 0000-0002-7796-1438
2011 (English)In: European Conference on Mobile Robots / [ed] Achim J. Lilienthal and Tom Duckett, 2011, 297-302 p.Conference paper (Refereed)
Abstract [en]

Robot localization using odometry and feature measurementsis a nonlinear estimation problem. An efficient solutionis found using the extended Kalman filter, EKF. The EKFhowever suffers from divergence and inconsistency when thenonlinearities are significant. We recently developed a new typeof filter based on an auxiliary variable Gaussian distributionwhich we call the antiparticle filter AF as an alternative nonlinearestimation filter that has improved consistency and stability. TheAF reduces to the iterative EKF, IEKF, when the posterior distributionis well represented by a simple Gaussian. It transitions to amore complex representation as required. We have implementedan example of the AF which uses a parameterization of the meanas a quadratic function of the auxiliary variables which we callthe quadratic antiparticle filter, QAF. We present simulationof robot feature based localization in which we examine therobustness to bias, and disturbances with comparison to the EKF.

Place, publisher, year, edition, pages
2011. 297-302 p.
Keyword [en]
Localization, Nonlinear Estimation, Robust Estimation
National Category
Robotics Control Engineering Signal Processing
URN: urn:nbn:se:kth:diva-48853OAI: diva2:458711
the 5th European Conference on Mobile Robots, ECMR 2011
QC 20120109Available from: 2012-01-09 Created: 2011-11-23 Last updated: 2012-01-17Bibliographically approved

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