Abstract
A novel perception system for autonomous navigation on low level roads and open terrain is presented. Built within the framework of the US-German AutoNav project, it combines UBM’s object oriented techniques, known as the 4D approach to machine perception (EMS-Vision), with Sarnoff’s hierarchical stereo processing.
The Vision Front End 200, a specially designed hardware device for real-time image processing, computes and evaluates 320-240 pixel disparity maps at 25 frames per second. A key element for this step is the calculation of the horopter, a virtual plane that is automatically locked to the ground plane. For improved reliability, the VFE 200 results are integrated over time in a grid-based terrain representation. Obstacle information can then be extracted. The system’s situation assessment generates a situation representation that consists of so-called situation aspects assigning symbolic attributes to scene objects. The behavior decision module combines this information with knowledge about its own body and behavioral capabilities to autonomously control the vehicle.
The system has been integrated into the experimental vehicle VaMoRs for autonomous mobility through machine perception. In a series of experiments, both positive and negative obstacles could be avoided at speeds of up to 16km/h (10mph).
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Siedersberger, KH. et al. (2001). Combining EMS-Vision and Horopter Stereo for Obstacle Avoidance of Autonomous Vehicles. In: Schiele, B., Sagerer, G. (eds) Computer Vision Systems. ICVS 2001. Lecture Notes in Computer Science, vol 2095. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-48222-9_10
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DOI: https://doi.org/10.1007/3-540-48222-9_10
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