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Don Gustafson and John Deyst. Anderson, Andrew D. (Andrew David) Massachusetts Institute of Technology. Dept. of Aeronautics and Astronautics. Massachusetts Institute of Technology. Department of Aeronautics and Astronautics 2007-02-21T11:53:00Z 2007-02-21T11:53:00Z 2006 2006 http://hdl.handle.net/1721.1/36174 74491691 Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 2006. Includes bibliographical references (p. 105-109). This thesis considers possible solutions to sample impoverishment, a well-known failure mode of the Rao-Blackwellized particle filter (RBPF) in simultaneous localization and mapping (SLAMI) situations that arises when precise feature measurements yield a limited perceptual distribution relative to a motion-based proposal distribution. One set of solutions propagates particles according to a more advanced proposal distribution that includes measurement information. Other methods recover lost sample diversity by resampling particles according to a continuous distribution formed by regularization kernels. Several advanced proposals and kernel shaping regularization methods are considered based on the RBPF and tested in a Monte Carlo simulation involving an agent traveling in an environment and observing uncertain landmarks. RMS error of range-bearing feature measurements was reduced to evaluate performance during proposal-perceptual distribution mismatch. A severe loss in accuracy due to sample impoverishment is seen in the standard RBPF at a measurement range RMS error of 0.001 m in a 10 m x 10 m environment. (cont.) Results reveal a robust and accurate solution to sample impoverishment in an RBPF with an added fixed-variance regularization algorithm. This algorithm produced an average 0.05 m improvement in agent pose CEP over standard FastSLAM 1.0 and a 0.1 m improvement over an RBPF that includes feature observations in formulation of a proposal distribution. This algorithm is then evaluated in an actual SLAM environment with data from a Swiss Ranger LIDAR measurement device and compared alongside an extended Kalman filter (EKF) based SLAM algorithm. Pose error is immediately recovered in cases of a 1.4 m error in initial agent uncertainty using the improved FastSLAM algorithm, and it continues to maintain an average 0.75 m improvement over an EKF in pose CEP through the scenario. by Andrew D. Anderson. S.M. 109 p. eng Massachusetts Institute of Technology M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission. http://dspace.mit.edu/handle/1721.1/7582 Aeronautics and Astronautics. Recovering sample diversity in Rao-Blackwellized particle filters for simultaneous localization and mapping Recovering sample diversity in RDPF for SLAMI Thesis application/pdf false Publication unknown unknown <Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="667402af-ce6a-4e4a-940b-352d7d5b91e2"> <Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type> <Language>eng</Language> <Title>Recovering sample diversity in Rao-Blackwellized particle filters for simultaneous localization and mapping</Title> <Subtitle>Recovering sample diversity in RDPF for SLAMI</Subtitle> <PublishedIn> <Publication> </Publication> </PublishedIn> <PublicationDate>2006</PublicationDate> <Authors> <Author> <DisplayName>Anderson, Andrew D. (Andrew David)</DisplayName> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> </Authors> <Editors> </Editors> <Publishers> <Publisher> <DisplayName>Massachusetts Institute of Technology</DisplayName> <OrgUnit /> </Publisher> </Publishers> <License>http://dspace.mit.edu/handle/1721.1/7582</License> <Keyword>Aeronautics and Astronautics.</Keyword> <Abstract>This thesis considers possible solutions to sample impoverishment, a well-known failure mode of the Rao-Blackwellized particle filter (RBPF) in simultaneous localization and mapping (SLAMI) situations that arises when precise feature measurements yield a limited perceptual distribution relative to a motion-based proposal distribution. One set of solutions propagates particles according to a more advanced proposal distribution that includes measurement information. Other methods recover lost sample diversity by resampling particles according to a continuous distribution formed by regularization kernels. Several advanced proposals and kernel shaping regularization methods are considered based on the RBPF and tested in a Monte Carlo simulation involving an agent traveling in an environment and observing uncertain landmarks. RMS error of range-bearing feature measurements was reduced to evaluate performance during proposal-perceptual distribution mismatch. A severe loss in accuracy due to sample impoverishment is seen in the standard RBPF at a measurement range RMS error of 0.001 m in a 10 m x 10 m environment.</Abstract> <Abstract>(cont.) Results reveal a robust and accurate solution to sample impoverishment in an RBPF with an added fixed-variance regularization algorithm. This algorithm produced an average 0.05 m improvement in agent pose CEP over standard FastSLAM 1.0 and a 0.1 m improvement over an RBPF that includes feature observations in formulation of a proposal distribution. This algorithm is then evaluated in an actual SLAM environment with data from a Swiss Ranger LIDAR measurement device and compared alongside an extended Kalman filter (EKF) based SLAM algorithm. Pose error is immediately recovered in cases of a 1.4 m error in initial agent uncertainty using the improved FastSLAM algorithm, and it continues to maintain an average 0.75 m improvement over an EKF in pose CEP through the scenario.</Abstract> <Access xmlns="http://purl.org/coar/access_right" > </Access> </Publication>