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.

The research field on intelligent environments that consist of many distributed sensors and robots has been expanding. Intelligent environments require the functions of extracting and tracking the multiple objects seamlessly to achieve appropriate services to users under the multi-camera environments. Some tracking systems often prepare the color models of the objects in advance. It is difficult to adopt these models to the tracking of the objects that change their color appearance according to the pose. However, model based tracking is efficient for the tracking in the crowded environment. And also, the object color models have to be valid for seamless tracking in the multi-camera environments. We propose the color histogram based object model configuration. This model termed global model is efficient for object tracking and matching. The eigen space of color histograms configured from color appearance in several poses of the object is utilized for the configuration of the global model. Color histogram models needed for tracking of the object that changed their appearance can be rebuilt from the global model. As results of the first experiments, the object correspondence among the different cameras was achieved empirically, and it was described that the proposed object model is effective for the seamless tracking by the distributed cameras