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We present a framework for the design and implementation of visually-guided, interactive, mobile robots. Essential to the framework's robust performance is our behavior-based robot control architecture enhanced with a state of the art decision-theoretic planner that takes into account the temporal characteristics of robot actions and allows us to achieve principled coordination of complex subtasks implemented as robot behaviors/skills. We study two different models of the decision theoretic layer: Multiply Sectioned Markov Decision Processes (MSMDPs) under the assumption that the world state is fully observable by the agent, and Partially Observable Markov Decision Processes (POMDPs) that remove the latter assumption and allow us to model the uncertainty in sensor measurements. The MSMDP model utilizes a divide-and-conquer approach for solving problems with millions of states using concurrent actions. For solving large POMDPs, we present heuristics that improve the computational efficiency of the point-based value iteration algorithm while tackling the problem of multi-step actions using Dynamic Bayesian Networks. In addition, we describe a state-of-the-art simultaneous localization and mapping algorithm for robots equipped with stereo vision. We first present the Monte-Carlo algorithm sigmaMCL for robot localization in 3D using natural landmarks identified by their appearance in images. Secondly, we extend sigmaMCL and develop the sigmaSLAM algorithm for solving the simultaneous localization and mapping problem for visually-guided, mobile robots. We demonstrate our real-time algorithm mapping large, indoor environments in the presence of large changes in illumination, image blurring and dynamic objects. Finally, we demonstrate empirically the applicability of our framework for developing interactive, mobile robots capable of completing complex tasks with the aid of a human companion. We present an award winning robot waiter for serving hors d'oeuvres at receptions and a robot for delivering verbal messages among inhabitants of an office-like environment.

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Most cameras detect colour by using sensors that separate red, green and blue wavelengths of light which is similar to the human eye. As such most colour information available for computer vision is represented in this trichromatic colour model, Red Green Blue or RGB. However this colour model is inadequate for most applications as objects requiring analysis are subject to the reflective properties of light, causing RGB colour to change across object surfaces. Many colour models have been borrowed from other disciplines which transform the RGB colour space into dimensions which are decorrelated to the reflective properties of light. Unfortunately signal noise is present in all acquired video, corrupting the image information. Fortunately most noise is statistically predictable, causing offsets from the true values following a Poisson distribution. When the standard deviation of a noise distribution is known, then noise can be stochastically predicted and accounted for. However transformations inside cameras and transformations between colour models often deform the image information in ways that make the noise distributions non-uniform over the colour model. When computer vision applications need to account for non-uniform noise, wider tolerances are required overall. This results in a loss of useful information and a reduction in discriminative power. This thesis has a focus on the linearity of signal noise distributions in colour representations which are decorrelated to the reflective properties of light. Existing colour models are described and each of their components examined with their strengths and weaknesses discussed. The results show that the proposed Signal Linear RGB (SLRGB) colour model achieves a transformation of the RGB colour space with uniform noise distributions along all axes under changes to camera properties. This colour space maintains a signal noise with a standard deviation of one unit across the space under changes of the camera parameters: white balance, exposure and gain. Experiments demonstrated that this proposed SLRGB model consistently provided improvements to linearity over RGB when used as a basis for other colour models. The proposed Minimum Weighted Colour Comparison (MWCC) method allows reflectively decorrelated colour models to make colour comparisons which counter the deforming effects of their coordinate systems. This was shown to provide substantial improvements to linearity tests in every case, making many colour models have a comparative noise linearity to undeformed colour models. The proposed Planar Hue Luminance Saturation (PHLS) and Spherical Hue Luminance Saturation (SHLS) colour models are decorrelated to reflective properties of light and allow for signal linear colour comparisons. When used for pixel classification of coloured objects the PHLS and SHLS colour models used only 0.26% and 0.25% of the colour volume to classify all of the objects, with the next best using 0.88% without MWCC and 0.45% with. The proposed Gamut Limit Invariant (GLI) colour model extends the decorrelation of reflective properties of light further by correcting for colours which are too bright and are clipped by the limits of the RGB space. When clipping occurs the properties become no longer decorrelated and shift. GLI models these changes to estimate the original values for clipped colours. The results show that this method improves decorrelation when performing pixel classification of coloured objects with varying proportions of clipped colours. Overall, the results show that the proposed framework of colour models and methods are a significant improvement over all prior colour models in enabling the most accurate information possible for processing colour images.