Detection of tree trunks as visual landmarks in outdoor environments
Author(s)
Advisor
Saranlı, UluçDate
2010Publisher
Bilkent University
Language
English
Type
ThesisItem Usage Stats
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Abstract
One of the basic problems to be addressed for a robot navigating in an outdoor
environment is the tracking of its position and state. A fundamental first step
in using algorithms for solving this problem, such as various visual Simultaneous
Localization and Mapping (SLAM) strategies, is the extraction and identification
of suitable stationary “landmarks” in the environment. This is particularly
challenging in the outdoors geometrically consistent features such as lines are not
frequent. In this thesis, we focus on using trees as persistent visual landmark
features in outdoor settings. Existing work to this end only uses intensity information
in images and does not work well in low-contrast settings. In contrast, we
propose a novel method to incorporate both color and intensity information as
well as regional attributes in an image towards robust of detection of tree trunks.
We describe both extensions to the well-known edge-flow method as well as complementary
Gabor-based edge detection methods to extract dominant edges in
the vertical direction. The final stages of our algorithm then group these vertical
edges into potential tree trunks using the integration of perceptual organization
and all available image features.
We characterize the detection performance of our algorithm for two different
datasets, one homogeneous dataset with different images of the same tree types
and a heterogeneous dataset with images taken from a much more diverse set of
trees under more dramatic variations in illumination, viewpoint and background
conditions. Our experiments show that our algorithm correctly finds up to 90%
of trees with a false-positive rate lower than 15% in both datasets. These results
establish that the integration of all available color, intensity and structure information
results in a high performance tree trunk detection system that is suitable
for use within a SLAM framework that outperforms other methods that only use
image intensity information.
Keywords
Edge detection, perceptual grouping, color, Gabor wavelets,Object detection
Tree trunk detection
Visual landmarks
Visual SLAM
Computer vision
Pattern recognition
Image processing
Perceptual grouping
Gabor wavelets