The baggage number needs to be checked automatically during baggage self-check-in. A fast airline baggage counting method is proposed in this paper using image segmentation based on height map which is projected by scanned baggage 3D point cloud. There is height drop in actual edge of baggage so that it can be detected by the edge detection operator. And then closed edge chains are formed from edge lines that is linked by morphological processing. Finally, the number of connected regions segmented by closed chains is taken as the baggage number. Multi-bag experiment that is performed on the condition of different placement modes proves the validity of the method.
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.
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