Abstract
Aiming at the problem of apple branch obstacle localization in fruit picking process of harvesting robot manipulator, in order to obtain three-dimensional information of the apple branch obstacle, the binocular stereo vision localization method of apple branch obstacle is proposed. Firstly, branch skeleton is extracted by morphological thinning method and then the feature skeleton is obtained after removing the false branch and recovering the occluded branch. After that, the endpoints and bifurcation points regarded as match feature points are extracted from skeleton, and the stereo matching algorithm based on features is adopted. Then, the depth information of branch obstacle is obtained on the basis of triangulation theory. Finally, the experiment results for apple tree branches localization show that the error lies in ±6.2 mm. Moreover, the error is merely ±1.5 mm when the distance between the object and the binocular camera is 1000 mm, which meets with localization accuracy requirements of apple harvesting robot visual system.
Highlights
The perception of fruit tree branches plays a very important role in obstacle avoidance and fruit picking process of harvesting robot manipulator
In our prior research of branch recognition,[1] we mainly focused on identifying the two-dimensional space of branches and did not included the depth information of branches
The branch skeleton is extracted from the morphological thinning
Summary
The perception of fruit tree branches plays a very important role in obstacle avoidance and fruit picking process of harvesting robot manipulator. In order to solve the problem of low localization accuracy which results from the rough edge of apples after segmentation, a new method based on a contour symmetry axis was proposed to locate the picking point of apples.[7] Bac et al.[8] researched about stem localization of sweet pepper plants using object-based and 3D features and minimum expected stem distance. From these references, the fruit and stem localization are usually a solution of the single point coordinates. The depth information of branch obstacle is obtained on the basis of triangulation theory
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