TAILIEUCHUNG - MIT.Press.Introduction.to.Autonomous.Mobile.Robots Part 10

Tham khảo tài liệu ' part 10', kỹ thuật - công nghệ, cơ khí - chế tạo máy phục vụ nhu cầu học tập, nghiên cứu và làm việc hiệu quả | 166 Chapter 4 a b Figure a Photo of a ceiling lamp. b Edges computed from a . Spatially localized features In the computer vision community many algorithms assume that the object of interest occupies only a sub-region of the image and therefore the features being sought are localized spatially within images of the scene. Local image-processing techniques find features that are local to a subset of pixels and such local features map to specific locations in the physical world. This makes them particularly applicable to geometric models of the robot s environment. The single most popular local feature extractor used by the mobile robotics community is the edge detector and so we begin with a discussion of this classic topic in computer vision. However mobile robots face the specific mobility challenges of obstacle avoidance and localization. In view of obstacle avoidance we present vision-based extraction of the floor plane enabling a robot to detect all areas that can be safely traversed. Finally in view of the need for localization we discuss the role of vision-based feature extraction in the detection of robot navigation landmarks. Edge detection. Figure shows an image of a scene containing a part of a ceiling lamp as well as the edges extracted from this image. Edges define regions in the image plane where a significant change in the image brightness takes place. As shown in this example edge detection significantly reduces the amount of information in an image and is therefore a useful potential feature during image interpretation. The hypothesis is that edge contours in an image correspond to important scene contours. As figure shows this is not entirely true. There is a difference between the output of an edge detector and an ideal line drawing. Typically there are missing contours as well as noise contours that do not correspond to anything of significance in the scene. Perception 167 The basic challenge of edge detection is visualized

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