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.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.