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dc.contributor.authorRamos, Fabio Tozeto
dc.date.accessioned2008-04-28
dc.date.available2008-04-28
dc.date.issued2007-02-01
dc.identifier.urihttp://hdl.handle.net/2123/2322
dc.description.abstractThis thesis addresses the problem of building statistical models for multi-sensor perception in unstructured outdoor environments. The perception problem is divided into three distinct tasks: recognition, representation and association. Recognition is cast as a statistical classification problem where inputs are images or a combination of images and ranging information. Given the complexity and variability of natural environments, this thesis investigates the use of Bayesian statistics and supervised dimensionality reduction to incorporate prior information and fuse sensory data. A compact probabilistic representation of natural objects is essential for many problems in field robotics. This thesis presents techniques for combining non-linear dimensionality reduction with parametric learning through Expectation Maximisation to build general representations of natural features. Once created these models need to be rapidly processed to account for incoming information. To this end, techniques for efficient probabilistic inference are proposed. The robustness of localisation and mapping algorithms is directly related to reliable data association. Conventional algorithms employ only geometric information which can become inconsistent for large trajectories. A new data association algorithm incorporating visual and geometric information is proposed to improve the reliability of this task. The method uses a compact probabilistic representation of objects to fuse visual and geometric information for the association decision. The main contributions of this thesis are: 1) a stochastic representation of objects through non-linear dimensionality reduction; 2) a landmark recognition system using a visual and ranging sensors; 3) a data association algorithm combining appearance and position properties; 4) a real-time algorithm for detection and segmentation of natural objects from few training images and 5) a real-time place recognition system combining dimensionality reduction and Bayesian learning. The theoretical contributions of this thesis are demonstrated with a series of experiments in unstructured environments. In particular, the combination of recognition, representation and association algorithms is applied to the Simultaneous Localisation and Mapping problem (SLAM) to close large loops in outdoor trajectories, proving the benefits of the proposed methodology.en
dc.rightsThe author retains copyright of this thesis.
dc.rights.urihttp://www.library.usyd.edu.au/copyright.html
dc.subjectfield robotics, dimensionality reduction, simultaneous localisation and mapping, probabilistic representationen
dc.titleRecognising, Representing and Mapping Natural Features in Unstructured Environmentsen
dc.typeThesisen_AU
dc.date.valid2008-01-01en
dc.type.thesisDoctor of Philosophyen_AU
usyd.facultyFaculty of Engineering and Information Technologies, School of Aerospace, Mechanical and Mechatronic Engineeringen_AU
usyd.departmentAustralian Centre for Field Roboticsen_AU
usyd.degreeDoctor of Philosophy Ph.D.en_AU
usyd.awardinginstThe University of Sydneyen_AU


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