Proceedings Article | 8 May 2010
KEYWORDS: Sensors, Reflection, Image processing, Algorithm development, Image resolution, Detector development, Detection and tracking algorithms, Visualization, Image classification, Image filtering
The Robotics Collaborative Technology Alliances (RCTA) program, which ran from 2001 to 2009, was funded by the
U.S. Army Research Laboratory and managed by General Dynamics Robotic Systems. The alliance brought together a
team of government, industrial, and academic institutions to address research and development required to enable the
deployment of future military unmanned ground vehicle systems ranging in size from man-portables to ground combat
vehicles. Under RCTA, three technology areas critical to the development of future autonomous unmanned systems
were addressed: advanced perception, intelligent control architectures and tactical behaviors, and human-robot
interaction. The Jet Propulsion Laboratory (JPL) participated as a member for the entire program, working four tasks in
the advanced perception technology area: stereo improvements, terrain classification, pedestrian detection in dynamic
environments, and long range terrain classification. Under the stereo task, significant improvements were made to the
quality of stereo range data used as a front end to the other three tasks. Under the terrain classification task, a multi-cue
water detector was developed that fuses cues from color, texture, and stereo range data, and three standalone water
detectors were developed based on sky reflections, object reflections (such as trees), and color variation. In addition, a
multi-sensor mud detector was developed that fuses cues from color stereo and polarization sensors. Under the long
range terrain classification task, a classifier was implemented that uses unsupervised and self-supervised learning of
traversability to extend the classification of terrain over which the vehicle drives to the far-field. Under the pedestrian
detection task, stereo vision was used to identify regions-of-interest in an image, classify those regions based on shape,
and track detected pedestrians in three-dimensional world coordinates. To improve the detectability of partially
occluded pedestrians and reduce pedestrian false alarms, a vehicle detection algorithm was developed. This paper
summarizes JPL's stereo-vision based perception contributions to the RCTA program.
