Differentiating Sonar Reflections from Corners and Planes by Employing an Intelligent Sensor
IEEE Transactions on Pattern Analysis and Machine Intelligence
Building a Sonar Map in a Specular Environment Using a Single Mobile Sensor
IEEE Transactions on Pattern Analysis and Machine Intelligence
Self-organizing maps
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Neural and statistical classifiers-taxonomy and two case studies
IEEE Transactions on Neural Networks
Journal of Intelligent and Robotic Systems
Old and new straight-line detectors: Description and comparison
Pattern Recognition
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Regarding assistance to disabled people for object manipulation and carrying, the paper focuses on the localisation for mobile robot autonomy. In order to respect strong low-cost constraints, the perception system of the mobile robot uses sensors of low metrological quality, ultrasonic ring and odometry. That poses new problems for localisation, in particular. Among different localisation techniques, we present only off-line localisation. With poor perception means, it is necessary to introduce a priori knowledge on sensors and environment models. To solve the localisation problem, the ultrasonic image is segmented applying the Hough transform, well-adapted to ultrasonic sensor characteristics. The segments are then matched with the room, modelled and assumed to be rectangular. Several positions are found. A first sort, based on a cost function, reduces the possibilities. The remaining ambiguities are removed by a neural network which plays the part of a classifier detecting the door in the environment. Improvements of the method are proposed to take into account obstacles and non-rectangular room. Experimental results show that the localisation operates even with one obstacle.