A new detection method for unstructured road based on robot's vision is proposed to improve the effectiveness of road
detection in complex environment. In this article, the OTSU, an auto-adapted threshold searching algorithm, is mainly
used to classify the road images. Meanwhile, to solve the problems of misclassification in complex environment, the
OTSU will be used the second time to subdivide. And multiple scene templates are built combining road referring
window (RRW). Then, multi-dimensional features are chosen for region reorganizing according to those templates to
obtain the optimal classification. At last, the classifying results are merged by referring RRW to extract the final road
region accurately. This algorithm shows good self-adaptive ability and only needs little priori knowledge. It is also
robust against noises, shadows and illumination variations and shows good real-time performance. It has been tested on
real robot and performed well in real road environment.
The precision of target sub-pixel centroid location directly affects the result of large scale vision 3D coordinates
measurement. This paper deeply studies the sub-pixel centroid location algorithm of retro-reflective targets and infrared
optical targets used in 3D coordinates measurement system, and makes use of improved cubic convolution interpolation algorithm to increase the number of effective pixels used in centroid location, then gives optimizing adjustment parameters for different types of targets and combined with squared gray weighted centroid location algorithm, finally realizes accurate target sub-pixel centroid location. This algorithm is proved to be effective and robust by simulations
and experiments.
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