In this paper, we propose an algorithm for human activity recognition based on Gaussian Process Classifier (GPC). A hierarchical strategy is firstly applied to classify dynamic and static behaviors. Then, in each layer, three kinds of classification approaches are validated and evaluated for promot ing recognition accuracy. Moreover, discriminative analysis method is invoked to cast high dimension features into lower dimensional space where classes are easily separated. Extensive experiments have been conducted and three vital points are observed: Firstly, GPC achieves comparable classification accuracy with other classifiers under the same experimental condition. Secondly, in case of less training samples, GPC outperforms the prominent Support Vector Machine (SVM) classifier. Thirdly, unlike SVM, GPC is more robust to the high dimensional features. Furthermore, we successfully implement the presented recognition algorithm into our hardware platform and achieve 99.75% accuracy on average in dealing with four sample activities.

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.