Semantic segmentation of manipulator grasping scene with fusion of RGB and depth information

Jiyuan Ding; Yanlang Mo; Xiaogang Xiong

doi:10.1117/12.2576971

12 October 2020 Semantic segmentation of manipulator grasping scene with fusion of RGB and depth information

Jiyuan Ding, Yanlang Mo, Xiaogang Xiong

Proceedings Volume 11574, International Symposium on Artificial Intelligence and Robotics 2020; 115740N (2020) https://doi.org/10.1117/12.2576971
Event: International Symposium on Artificial Intelligence and Robotics (ISAIR), 2020, Kitakyushu, Japan

Abstract

Semantic segmentation with image RGB information is significantly useful for intelligent perception of robotics. However, semantic segmentation with only RGB information does not perform well for objects with the same color during grasping manipulation. This paper proposes a new semantic segmentation scheme based on the fusion of RGB and heights transformed from depth information, which is not simple fusion of RGB-D method. It modifies the height information so that different objects of the same color can be distinguished in height. It outperforms the classical RGB segmentation scheme at improving speed and 7.42% higher at the final performance of semantic segmentation of manipulator grasping scene (contains objects with the same color). Because of the need of RGB-D information, this paper proposes a method of self-collecting and self-labeling data of manipulator grasping scene, which reduces the cost of manpower by making full use of the highly automated equipment and the characteristics of the scene.

Citation Download Citation

Jiyuan Ding, Yanlang Mo, and Xiaogang Xiong "Semantic segmentation of manipulator grasping scene with fusion of RGB and depth information", Proc. SPIE 11574, International Symposium on Artificial Intelligence and Robotics 2020, 115740N (12 October 2020); https://doi.org/10.1117/12.2576971

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KEYWORDS

Image segmentation

RGB color model

Cameras

Imaging systems

Convolution

Image fusion

Neural networks

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