1991

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  1. Dudek, G., Jenkin, M., Milios, E. and Wilkes, D., Sonar sensing and obstacle detection, Proc 3rd Conference on Military Robotics Applications, Medicine Hat, Alberta, 198-205, 1991.
    The combined problems of sensor error modelling and the exploration of unknown environments are fundamental aspects of autonomous robotics. We consider these problems in the context of mapping an unknown close enviornment with a sonar sensor.This leads to three issues at different levels of abstraction: modelling the characteristics of the sensor, dealing with the unavoidable anomalies in teh data obtained, and constructing a long-term representation of the enviornment. An important aspect of this partitioning of the problem is that it takes us from local quantitative descriptions large-scale symbolic ones.This paper presents algorithms for extracting obstacle contours from sonar data, that accounts for the properties of sonar modelled in our previous work. THis work is then related to work on the high-level problem of construction a map of an unknown environment.

    Our model of sonar range sensing for robot navigation accounts for multiple reflections of the sonar signal between transmission and reception. This gives more realistic results than previous models. This controlled data-acquisition methodology can be subsequently used to compare techniques for the interpretation of the acquired data.

    From this model of sonar sensing itself, a new model for inferring coherent reflecting surfaces is developed based on an assumption of coherent smooth surfaces in the world. This is accomplished by using an "energy based minimization" algorithm to reject sonar measurements that are not consistent with an a priori world model.

    Finally we briefly discuss a representation for large scale structure of the enviornment that is based only on the topological interrelation between places of interest and the paths connecting them.

  2. Dudek, G., Jenkin, M., Milios, E. and Wilkes, D. Robotic exploration as graph construction. IEEE Transactions on Robotics and Automation, 7: 859-864, 1991.
    We address the problem of robotic exploration of a graph-like world, where no distance or orientation metric is assumed of the world. The robot is assumed to be able to autonomously traverse graph edges, recongize when it has reached a vertex, and enumerate edges incident upon the current vertex relative to the edge via which it entered the current vertex. The robot cannot measure distances, and it does not have a compass. We demonstrate that this exploration problem is unsolvable in general without markers, and, to solve it, we equip the robot with one or more distinct markers that can be put down or picked up at will and that can be recongized by the robot if they are at the same vertex as the robot. We develop and prove correct an exploration algorith, we show its performance on several example worlds, and we discuss heuristics for improving its performance.
  3. Jenkin, M. R. M., Using stereomotion to track binocular targets. Proc. IEEE CVPR 91, 95-102, 1991.
    The construction of binocular heads has lead to an increased interest in the control of binocular eye movements by the computer vision community. This paper presents an algorithm for the smooth tracking of a target in three space by a binocular head which is capable of vergence, version, and tilt eye movements. This algorithm utilizes stereomotion channels to obtain a measurement of the three dimensional velocity of the target, and then uses this velocity within a control loop to keep the target centred at the fixation point of the binocular head. Although stereomotion alone is insufficient to accurately drive binocular eye movements, relative stereomotion is a useful measurement to help drive binocular eye movements and coulbe be easily integrated into a positional error driven tracking system.
  4. Jenkin, M. R. M., Jepson, A. D. and Tsotsos, J. K. Techniques for disparity measurement.CVGIP: Image Understanding, 53: 14-30, 1991.
    Many different approaches have been suggested for the measurement of structure in space from spatially separated cameras. In this report we critically examine some of these techniques. Through a series of examples we show that none of the current mechanisms of disparity measurement are particularly robust. By considering some of the implications of disaprity in the frequency domain, we present a new definition of disparity that is tied to the interacular phase difference in bandpass versions of the monocular images. Finally, we present a new technique for measuring disparity as the local phase difference between bandpass versions of the two images, and we show how this technique surmounts some the difficulties encountered by current disparity detection mechanisms.
  5. Fleet, D. J., Jepson, A. D. and Jenkin, M. R. M. Improving phase-based disparity measurement. CVGIP: Image Understanding, 53: 198-210, 1991. Note: The pdf file is from an earlier draft of the manuscript with a slightly different title.
    The measurement of image disparity is a fundamental precursor to binocular depth estimation. Recently, Jenkin and Jepson (1988) and Sanger (1988) described promising methods based on the output phase behaviour of band-pass Gabor filters. Here we discuss further justification for such techniques based on the stabililty of band-pass phase behaviour as a function of typical distortions that exist between left and right views. In addition, despite this general stability, we show that phase signals are occasionally very sensitive to spatial position and variations in scale, in which case incorrect measurements occur. We find that the primary case for this instability is the existence of signularities in phase signals. With the aid of the local frequency of the filter output (provided by the phase derivative) and the local amplitude information, the regions of phase instability near the singularities are detected so that potential incorrect measurements can be identified. In addition, we show how the local frequency can be used away from the singularity neighbourhoods to improve the accuracy of the disparity estimates. Some experimental results are reported.
  6. Wildes, D., Dudek, G., Jenkin, M., and Milios, E. A multi-surface model of sonar range sensing. Proc. Vision Interface '91, 213-219, 1991.
    We present a simulation-based model of sonar range sensing for robot navigation that accounts for multiple reflections of the sonar signal between transmission and reception. The model also accounts for environments with surfaces have different reflectances. This gives more realistic results than previous models. The approach is based on simulation of the reflection and diffraction of sonar rays off reflecting surfaces until they are attenuated or return to the receiver. Parmaeters of the model include frequency, minimum and maximum range, and signal detection threshold (relative to emitted signal strength, after linear gain compensation) and environmental characteristics.
  7. Wang, Z. and Jenkin, M. Phase-based edge and bar detection. Proc. Vision Interface '91, 97-102, 1991.
    Edges and bars are low level features which can be used as primitives for complex visual tasks. This paper considers the detection and localization of one dimensional edges and bar-like targets of a particular width. The algorithms use local phase information obtained by filtering the image with a Gabor kernel to identify edge and bar-like structures, utilize local frequency and amplitude information to reject measurements from particular regions where the Gabor output may be suspect, and to rank the measurements that are recovered. Applications of the algorithms to real images indicate the promise of this approach.
  8. Wang, Z. and Jenkin, M. Using complex Gabor filters to detect and localize edges and bars. In C. Archibald and E. Petriu (eds.) Advances in Machine Learning: Strategies and Applications, Chapter 8, pp. 151-170.
    Early vision process is the extraction of primitive measurements from the image, and this forms the first stage of computation in many vision systems. This paper presents a new approach to feature detection problem by transforming the problem to a complex space in which features may be more readily and robustly recognized. Aiming at providing a computational way of producing feature maps, we first define mathematically the feature of interest, then study phase, amplitude (local energy) and local frequency behaviour of the feature to derive criteria for its detection. This feature detection technique is applied to edges and bars, which are low level features used as primitives for complex visual tasks. We consider the use of phase, amplitude and local frequency information for edge and bar identification as an alternative to numerical differentiation, which is known to be unstable in the response of noise. Algorithms for the detection and localization of one dimensional edges and bar-like targets of a particular width are developed and their application to real images indicate the promise of this approach.