Share Email Print
cover

Proceedings Paper

Three-dimensional obstacle classification in laser range data
Format Member Price Non-Member Price
PDF $14.40 $18.00

Paper Abstract

The threat of hostile surveillance and weapon systems require military aircraft to fly under extreme conditions such as low altitude, high speed, poor visibility and incomplete terrain information. The probability of collision with natural and man-made obstacles during such contour missions is high if detection capability is restricted to conventional vision aids. Forward-looking scanning laser rangefinders which are presently being flight tested and evaluated at German proving grounds, provide a possible solution, having a large field of view, high angular and range resolution, a high pulse repetition rate, and sufficient pulse energy to register returns from wires at over 500 m range (depends on the system) with a high hit-and-detect probability. Despite the efficiency of the sensor, acceptance of current obstacle warning systems by test pilots is not very high, mainly due to the systems' inadequacies in obstacle recognition and visualization. This has motivated the development and the testing of more advanced 3d-scene analysis algorithm at FGAN-FIM to replace the obstacle recognition component of current warning systems. The basic ideas are to increase the recognition probability and to reduce the false alarm rate for hard-to-extract obstacles such as wires, by using more readily recognizable objects such as terrain, poles, pylons, trees, etc. by implementing a hierarchical classification procedure to generate a parametric description of the terrain surface as well as the class, position, orientation, size and shape of all objects in the scene. The algorithms can be used for other applications such as terrain following, autonomous obstacle avoidance, and automatic target recognition.

Paper Details

Date Published: 26 October 1998
PDF: 9 pages
Proc. SPIE 3436, Infrared Technology and Applications XXIV, (26 October 1998); doi: 10.1117/12.328046
Show Author Affiliations
Walter Armbruster, FGAN-Research Institute for Information Processing and Pattern Recognition (Germany)
Karl-Heinz Bers, FGAN-Research Institute for Information Processing and Pattern Recognition (Germany)


Published in SPIE Proceedings Vol. 3436:
Infrared Technology and Applications XXIV
Bjorn F. Andresen; Marija Strojnik, Editor(s)

© SPIE. Terms of Use
Back to Top