In order for an autonomous military robot to "appropriately" navigate through a complex environment, it must have an in-depth understanding of the immediate surroundings. We have developed a scene understanding system based on a multi-sensor system that uses an "operator-trained" rule-base to analyze the pixel level attributes across the set of diverse phenomenology imaging sensors. Each pixel is registered to range information so we not only know what but where features are in the environment. This three dimensional labeled world model can then be used to control the speed and steering of the vehicle in an appropriate manner. In this paper we discuss our multi-sensor system, the operator trained analysis algorithm called ONAV (opportunistic navigation), and the reactive control algorithm used to control the speed and steering of the vehicle.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    A high fidelity multi-sensor scene understanding system for autonomous navigation


    Contributors:
    Rosenblum, M. (author) / Gothard, B. (author)


    Publication date :

    2000-01-01


    Size :

    629343 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    A High Fidelity Multi-Sensor Scene Understanding System for Autonomous Navigation

    Rosenblum, M. / Gothard, B. / IEEE | British Library Conference Proceedings | 2000




    Multi-sensor navigation system for an autonomous helicopter

    Dittrich, J.S. / Johnson, E.N. | IEEE | 2002


    Auditory Scene Understanding for Autonomous Driving

    Furletov, Yury / Willert, Volker / Adamy, Jurgen | IEEE | 2021