Clustering methods for removing outliers from vision-based range estimates

Bassam Hussien; Raymond E. Suorsa

doi:10.1117/12.135099

1 March 1992 Clustering methods for removing outliers from vision-based range estimates

Bassam Hussien, Raymond E. Suorsa

Proceedings Volume 1608, Intelligent Robots and Computer Vision X: Neural, Biological, and 3-D Methods; (1992) https://doi.org/10.1117/12.135099
Event: Robotics '91, 1991, Boston, MA, United States

Abstract

The automation of rotorcraft low-altitude flight presents challenging problems in flight control and sensor systems. The currently explored approach uses one or more passive sensors, such as a television camera, to extract environmental obstacle information. Obstacle imagery can be processed using a variety of computer vision techniques to produce a time-varying map of range to obstacles in the sensor's field of view along the helicopter flight path. To maneuver in tight space, obstacle-avoidance methods would need very reliable range map information by which to guide the helicopter through the environment. In general, most low level computer vision techniques generate sparse range maps which include at least a small percentage of bad estimates (outliers). This paper examines two related techniques which can be used to eliminate outliers from a sparse range map. Each method clusters sparse range map information into different spatial classes relying on a segmented and labeled image to help in spatial classification within the image plane.

Citation Download Citation

Bassam Hussien and Raymond E. Suorsa "Clustering methods for removing outliers from vision-based range estimates", Proc. SPIE 1608, Intelligent Robots and Computer Vision X: Neural, Biological, and 3-D Methods, (1 March 1992); https://doi.org/10.1117/12.135099

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