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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...
A feasible solution to the Simultaneous Localization and Mapping (SLAM) problem is often considered invaluable in many autonomous vehicle navigation applications. In this paper we show that SLAM initialized with a known vehicle pose can be considered as a problem of parameter identification. Using a rank test for nonlinear map state identification, we establish that all the map states in the SLAM...
This paper investigates the Centralized Multi-vehicle Simultaneous Localization and Mapping (CMSLAM) problem in the context of the nonlinear observability. Theory is first developed for the nonlinear observability of CMSLAM using the relatively simple unicycle vehicle model, which gives rise to a CMSLAM problem in control affine form. Conditions required for nonlinear observability of CMSLAM when...
Notable problems in Simultaneous Localization and Mapping (SLAM) are caused by biases and drifts in both exteroceptive and proprioceptive sensors. The impacts of sensor biases include inconsistent map estimates and inaccurate localization. Unlike Map Aided Localisation with Joint Sensor Bias Estimation (MAL-JSBE), SLAM with Joint Sensor Bias Estimation (SLAM-JSBE) is more complex as it encompasses...
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