Triaural perception: a comparison of neural network and maximum likelihood algorithms to solve the correspondence problem

Herbert Peremans; J. Chen

doi:10.1117/12.193951

9 November 1994 Triaural perception: a comparison of neural network and maximum likelihood algorithms to solve the correspondence problem

Herbert Peremans, J. Chen

Author Affiliations +

Proceedings Volume 2247, Sensors and Control for Automation; (1994) https://doi.org/10.1117/12.193951
Event: Optics for Productivity in Manufacturing, 1994, Frankfurt, Germany

Abstract

To solve some of the problems associated with using conventional ultrasonic range sensors for mobile robots, we propose the use of tri-aural sensors. A tri-aural sensor consists of one ultrasonic transceiver and two additional receivers. With it the robot can determine accurate position estimates, both distance and bearing, of most of the objects in its field of view. This sensor also has object recognition capabilities, making it possible to discriminate between edges and planes. However, this information is available only if the echoes detected by the three receivers can be combined in groups consisting of echoes generated by the same reflector. This problem is very similar to the matching problem in stereo-vision. In this paper we compare two matching algorithms. One based on the maximum likelihood principle. The other based on a multi-layer perceptron neural network. To test how these matching algorithms fare in realistic circumstances we have done a number of simulations. The results are discussed in the final section of the paper.

Citation Download Citation

Herbert Peremans and J. Chen "Triaural perception: a comparison of neural network and maximum likelihood algorithms to solve the correspondence problem", Proc. SPIE 2247, Sensors and Control for Automation, (9 November 1994); https://doi.org/10.1117/12.193951

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