Abstract
This paper proposes a novel method for computing robot motion uncertainty from ranging sensor data. The method utilises the recently proposed CRF-Matching procedure which matches laser scans based on shape descriptors. Motion estimates are computed in a probabilistic framework by performing inference on a probabilistic graphical model. We propose an efficient sampling procedure for obtaining probable association hypothesis of the probabilistic graphical model. The hypothesis are used to generate estimates on the uncertainty of translational and rotational movements of the robot. Experiments demonstrate the benefits of the approach on simulated data sets and on laser scans from an urban environment. The approach is also combined with the well-established delayed-state information filter for a large-scale outdoor simultaneous localisation and mapping task.
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