Abstract
In the safety area of industrial production, robots are required to have the ability of quickly detecting the dynamic obstacles which appear in the working space of robots when robots move along a predefined route. In order to solve the problem of dynamic obstacles detection more effectively, the paper proposes a quick detection method based on background compensation for dynamic obstacles in robot movement space, which makes extraction of feature points on dynamic obstacles in robot movement space, establishes the background model of optical flow velocity field between adjacent frames and makes background compensation for images with the use of block-matching algorithm in order to realize quick and accurate dynamic obstacles detection in dynamic environment. The experiment results show that the obstacles detection method can effectively eliminate the background movement caused by the movement of camera at the end of robot and can extract a more complete target object. Moreover, it also has good robustness.
Highlights
Dynamic obstacle detection based on background compensation in the robot movement space mainly includes static scene and dynamic scene
The method adopted to build background model is to calculate optical flow velocity field background model for feature points extracted from m u n non-overlapping bits with the same algorithm, which can improve the accuracy of matching between feature points effectively as well as decrease calculation time
The good background compensation is obtained in the dynamic scene, and dynamic obstacles are detected with the optical flow method in static scene by analyzing and resolving robot’s camera six parameters model and combining with block matching optical flow velocity field model
Summary
Dynamic obstacle detection based on background compensation in the robot movement space mainly includes static scene and dynamic scene. Moving target detection under complex background mainly includes the following three ways: the background model method [2], inter-frame difference method [3] and the optical flow field method [4]. We start from the extraction of moving velocity of feature pixel in grayscale images [5], calculate the optical flow velocity in pixel points using the method based on block matching algorithm, and establish an optical flow velocity field background model for two frames front and behind, and compensate for background movement in the moving space, accomplish a moving obstacle detection. The method adopted to build background model is to calculate optical flow velocity field background model for feature points extracted from m u n non-overlapping bits with the same algorithm, which can improve the accuracy of matching between feature points effectively as well as decrease calculation time
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