The objective of this thesis is to develop a general obstacle segmentation
algorithm for use on board a ground based unmanned vehicle (GUV). The algorithm
processes video data captured by a single monocular camera mounted on the GUV. We
make the assumption that the GUV moves on a locally planar surface, representing the
ground plane. We start by deriving the equations of the expected motion field (observed
by the camera) induced by the motion of the robot on the ground plane. Given an initial
view of a presumably static scene, this motion field is used to generate a predicted view
of the same scene after a known camera displacement. This predicted image is compared
to the actual image taken at the new camera location by means of an optical flow
calculation. Because the planar assumption is used to generate the predicted image,
portions of the image which mismatch the prediction correspond to salient feature points
on objects which lie above or below the ground plane, we consider these objects
obstacles for the GUV. We assume that these salient feature points (called seed pixels )
capture the color statistics of the obstacle and use them to initialize a Bayesian region
growing routine to generate a full obstacle segmentation. Alignment of the seed pixels
with the obstacle is not guaranteed due to the aperture problem, however successful
segmentations were obtained for natural scenes. The algorithm was tested off line using
video captured by a camera mounted on a GUV.
| Identifer | oai:union.ndltd.org:GATECH/oai:smartech.gatech.edu:1853/24697 |
| Date | 19 May 2008 |
| Creators | Goroshin, Rostislav |
| Publisher | Georgia Institute of Technology |
| Source Sets | Georgia Tech Electronic Thesis and Dissertation Archive |
| Detected Language | English |
| Type | Thesis |
Page generated in 0.0022 seconds