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This paper addresses the problem of large scale terrain modeling for a mobile robot. Building a model of large scale terrain data that can adequately handle uncertainty and incompleteness in a statistically sound way is a very challenging problem. This work proposes the use of Gaussian Processes as models of large scale terrain. The proposed model naturally(More)
— Generating rich representations of environments can significantly improve the autonomy of mobile robotics. In this paper we introduce a novel approach to building object-type maps of outdoor environments. Our approach uses conditional random fields (CRF) to jointly classify laser returns in a 2D scan map into seven object types (car, wall, tree trunk,(More)
This paper investigates the possibility of recognising individual persons from their walking gait using three-dimensional 'skeleton' data from an inexpensive consumer-level sensor, the Microsoft 'Kinect'. In an experimental pilot study it is shown that the K-means algorithm - as a candidate unsupervised clustering algorithm - is able to cluster gait samples(More)
Determine a non-myopic solution to the sequential decision making problem of monitoring and optimising a space and time dependent function using a moving sensor. Contributions: Sequential Bayesian Optimisation (SBO) Formulate SBO as a Partially Observed Markov Decision Process (POMDP). Find non-mypic solution for the POMDP analog of SBO using Monte-Carlo(More)
We present algorithms for the generation of uniformly distributed Bayesian networks with constraints on induced width. The algorithms use ergodic Markov chains to generate samples. The introduction of constraints on induced width leads to realistic networks but requires new techniques. A tool that generates random networks is presented and applications are(More)
Place recognition is a challenging task in any SLAM system. Algorithms based on visual appearance are becoming popular to detect locations already visited, also known as loop closures, because cameras are easily available and provide rich scene detail. These algorithms typically result in pairs of images considered depicting the same location. To avoid(More)
This paper addresses the problem of using visual information to estimate vehicle motion (a.k.a. visual odometry) from a machine learning perspective. The vast majority of current visual odometry algorithms are heavily based on geometry, using a calibrated camera model to recover relative translation (up to scale) and rotation by tracking image features over(More)
—The vast amount of data robots can capture today motivates the development of fast and scalable statistical tools to model the environment the robot operates in. We devise a new technique for environment representation through continuous occupancy mapping that improves on the popular occupancy grip maps in two fundamental aspects: 1) it does not assume an(More)
This paper presents a robust stochastic framework for the incorporation of visual observations into conventional estimation, data fusion, navigation and control algorithms. The representation combines Isomap, a non-linear dimensionality reduction algorithm, with expectation maximization, a statistical learning scheme. The joint probability distribution of(More)