A method for stereo-vision-based tracking for robotic applications

Vision-based tracking of an object using perspective projection inherently results in non-linear measurement equations in the Cartesian coordinates. The underlying object kinematics can be modelled by a linear system. In this paper we introduce a measurement conversion technique that analytically transforms the non-linear measurement equations obtained from a stereo-vision system into a system of linear measurement equations.We then design a robust linear filter around the converted measurement system. The state estimation error of the proposed filter is bounded and we provide a rigorous theoretical analysis of this result. The performance of the robust filter developed in this paper is demonstrated via computer simulation and via practical experimentation using a robotic manipulator as a target. The proposed filter is shown to outperform the extended Kalman filter (EKF).

A robust solution to the stereo-vision-based simultaneous localization and mapping problem with steady and moving landmarks

The problem of visual simultaneous localization and mapping (SLAM) is examined in this paper using recently developed ideas and algorithms from modern robust control and estimation theory. A nonlinear model for a stereo-vision-based sensor is derived that leads to nonlinear measurements of the landmark coordinates along with optical flow-based measurements of the relative robot-landmark velocity. Using a novel analytical measurement transformation, the nonlinear SLAM problem is converted into the linear domain and solved using a robust linear filter. Actually, the linear filter is guaranteed stable and the SLAM state estimation error is bounded within an ellipsoidal set. A mathematically rigorous stability proof is given that holds true even when the landmarks move in accordance with an unknown control input. No similar results are available for the commonly employed extended Kalman filter, which is known to exhibit divergence and inconsistency characteristics in practice. A number of illustrative examples are given using both simulated and real vision data that further validate the proposed method.

A method for stereo-vision based tracking for robotic applications

Vision based tracking of an object using the ideas of perspective projection inherently consists of nonlinearly modelled measurements although the underlying dynamic system that encompasses the object and the vision sensors can be linear. Based on a necessary stereo vision setting, we introduce an appropriate measurement conversion techniques which subsequently facilitate using a linear filter. Linear filter together with the aforementioned measurement conversion approach conforms a robust linear filter that is based on the set values state estimation ideas; a particularly rich area in the robust control literature. We provide a rigorously theoretical analysis to ensure bounded state estimation errors formulated in terms of an ellipsoidal set in which the actual state is guaranteed to be included to an arbitrary high probability. Using computer simulations as well as a practical implementation consisting of a robotic manipulator, we demonstrate our linear robust filter significantly outperforms the traditionally used extended Kalman filter under this stereo vision scenario. © 2008 IEEE.

Cost analysis error? Exploring issues relating to whole-life cost estimation in sustainable housing

Purpose: The main purpose of the study is to promote consideration of the issues and approaches available for costing sustainable buildings with a view to minimising cost overruns, occasioned by conservative whole-life cost estimates. The paper primarily looks at the impact of adopting continuity in whole-life cost models for zero carbon houses. Design/methodology/approach: The study embraces a mathematically based risk procedure based on the binomial theorem for analysing the cost implication of the Lighthouse zero-carbon house project. A practical application of the continuous whole-life cost model is developed and results are compared with existing whole-life cost techniques using finite element methods and Monte Carlo analysis. Findings: With standard whole-life costing, discounted present-value analysis tends to underestimate the cost of a project. Adopting continuity in whole-life cost models presents a clearer picture and profile of the economic realities and decision-choices confronting clients and policy-makers. It also expands the informative scope on the costs of zero-carbon housing projects. Research limitations/implications: A primary limitation in this work is its focus on just one property type as the unit of analysis. This research is also limited in its consideration of initial and running cost categories only. The capital cost figures for the Lighthouse are indicative rather than definitive. Practical implications: The continuous whole-life cost technique is a novel and innovative approach in financial appraisal [...] Benefits of an improved costing framework will be far-reaching in establishing effective policies aimed at client acceptance and optimally performing supply chain networks. Originality/value: The continuous whole-life costing pioneers an experimental departure from the stereo-typical discounting mechanism in standard whole-life costing procedures.