A Random-Finite-Set Approach to Bayesian SLAM

Abstract

Artículo de publicación ISIThis paper proposes an integrated Bayesian framework for feature-based simultaneous localization and map building (SLAM) in the general case of uncertain feature number and data association. By modeling the measurements and feature map as random finite sets (RFSs), a formulation of the feature-based SLAM problem is presented that jointly estimates the number and location of the features, as well as the vehicle trajectory. More concisely, the joint posterior distribution of the set-valued map and vehicle trajectory is propagated forward in time as measurements arrive, thereby incorporating both data association and feature management into a single recursion. Furthermore, the Bayes optimality of the proposed approach is established. A first-order solution, which is coined as the probability hypothesis density (PHD) SLAM filter, is derived, which jointly propagates the posterior PHD of the map and the posterior distribution of the vehicle trajectory. A Rao-Blackwellized (RB) implementation of the PHD-SLAM filter is proposed based on the Gaussian-mixture PHD filter (for the map) and a particle filter (for the vehicle trajectory). Simulated and experimental results demonstrate the merits of the proposed approach, particularly in situations of high clutter and data association ambiguity.Singapore National Research Foundation through the Singapore-Massachusetts Institute of Technology Alliance for Research and Technology Center for Environmental Sensing and Modeling Australian Research Council DP0880553 DP098900

Single cluster PHD SLAM: Application to autonomous underwater vehicles using stereo vision

“…The reader is referred to the work of Mullane et al [9], [10] and Lee, Clark & Salvi [12], [13] for an in-depth treatment of the PHD-based SLAM methods. Additionally, the reader is referred to the works of Mahler, B.-N. Vo, B.-T. Vo, Ristic and Clark [7], [15]- [21] for further information on PHD filters.…”

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“…B. Kalyan [30] and John. M [31] implemented the PHD filter in the field of the simultaneous localization and mapping (SLAM) problem. Results illustrates that the PHD filter is an effective solution to the SLAM problem.…”

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