Rao-Blackwellised RFS Bayesian SLAM
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This chapter proposes an alternative Bayesian framework for feature-based SLAM, again in the general case of uncertain feature number and data association. As in Chapter 5, a first order solution, coined the probability hypothesis density (PHD) SLAM filter, is used, which jointly propagates the posterior PHD of the map and the posterior distribution of the vehicle trajectory. In this chapter however, a Rao-Blackwellised (RB) implementation of the PHD-SLAM filter is proposed based on the GM PHD filter for the map and a particle filter for the vehicle trajectory, with initial results presented in  and further refinements in  .
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Mullane, J.; Vo, Ba-Ngu; Adams, M.; Vo, B. (2011)The previous chapter provided the motivation to adopt an RFS representation for the map in both FBRM and SLAM problems. The main advantage of the RFS formulation is that the dimensions of the measurement likelihood and ...
Adams, M.; Mullane, J.; Vo, Ba-Ngu (2013)In autonomous applications, a vehicle requires reliable estimates of its location and information about the world around it. To capture prior knowledge of the uncertainties in a vehicle's motion response to input commands ...
Mullane, J.; Vo, Ba-Ngu; Adams, M.; Vo, Ba Tuong (2011)This paper proposes an integrated Bayesian frame work for feature-based simultaneous localization and map building (SLAM) in the general case of uncertain feature number and data association. By modeling the measurements ...