8 May 2010 Detecting and tracking moving objects from a moving platform using epipolar constraints
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Proceedings Volume 7692, Unmanned Systems Technology XII; 76920K (2010); doi: 10.1117/12.852969
Event: SPIE Defense, Security, and Sensing, 2010, Orlando, Florida, United States
Abstract
One of the principal challenges in autonomous navigation for mobile ground robots is collision avoidance, especially in dynamic environments featuring both moving and non-moving (static) obstacles. Detecting and tracking moving objects (such as vehicles and pedestrians) presents a particular challenge because all points in the scene are in motion relative to a moving platform. We present a solution for detecting and robustly tracking moving objects from a moving platform. We use a novel epipolar Hough transform to identify points in the scene which do not conform to the geometric constraints of a static scene when viewed from a moving camera. These points can then be analyzed in three different domains: image space, Hough space and world space, allowing redundant clustering and tracking of moving objects. We use a particle filter to model uncertainty in the tracking process and a multiple-hypothesis tracker with lifecycle management to maintain tracks through occlusions and stop-start conditions. The result is a set of detected objects whose position and estimated trajectory are continuously updated for use by path planning and collision avoidance systems. We present results from experiments using a mobile test robot with a forward looking stereo camera navigating among multiple moving objects.
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Jonah C. McBride, Andrey Ostapchenko, Howard Schultz, Magnus S. Snorrason, "Detecting and tracking moving objects from a moving platform using epipolar constraints", Proc. SPIE 7692, Unmanned Systems Technology XII, 76920K (8 May 2010); doi: 10.1117/12.852969; https://doi.org/10.1117/12.852969
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KEYWORDS
Robots

Particles

Particle filters

Hough transforms

Cameras

Stereoscopic cameras

Navigation systems

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