Paper
12 May 2006 Using advanced computer vision algorithms on small mobile robots
Author Affiliations +
Abstract
The Technology Transfer project employs a spiral development process to enhance the functionality and autonomy of mobile robot systems in the Joint Robotics Program (JRP) Robotic Systems Pool by converging existing component technologies onto a transition platform for optimization. An example of this approach is the implementation of advanced computer vision algorithms on small mobile robots. We demonstrate the implementation and testing of the following two algorithms useful on mobile robots: 1) object classification using a boosted Cascade of classifiers trained with the Adaboost training algorithm, and 2) human presence detection from a moving platform. Object classification is performed with an Adaboost training system developed at the University of California, San Diego (UCSD) Computer Vision Lab. This classification algorithm has been used to successfully detect the license plates of automobiles in motion in real-time. While working towards a solution to increase the robustness of this system to perform generic object recognition, this paper demonstrates an extension to this application by detecting soda cans in a cluttered indoor environment. The human presence detection from a moving platform system uses a data fusion algorithm which combines results from a scanning laser and a thermal imager. The system is able to detect the presence of humans while both the humans and the robot are moving simultaneously. In both systems, the two aforementioned algorithms were implemented on embedded hardware and optimized for use in real-time. Test results are shown for a variety of environments.
© (2006) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
G. Kogut, F. Birchmore, E. Biagtan Pacis, and H. R. Everett "Using advanced computer vision algorithms on small mobile robots", Proc. SPIE 6230, Unmanned Systems Technology VIII, 623021 (12 May 2006); https://doi.org/10.1117/12.666188
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CITATIONS
Cited by 6 scholarly publications.
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KEYWORDS
Video

Detection and tracking algorithms

Computer vision technology

Machine vision

Mobile robots

Motion detection

Sensors

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