SpaceYNet: A Novel Approach to Pose and Depth-Scene Regression Simultaneously
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Data
2020-07-01
Autores
Aragao, Dunfrey
Nascimento, Tiago
Mondini, Adriano [UNESP]
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Resumo
One of the fundamental dilemmas of mobile robotics is the use of sensory information to locate an agent in geographic space. In this paper, we developed a global relocation system to predict the robot's position and avoid unforeseen actions from a monocular image, which we named SpaceYNet. We incorporated Inception layers to symmetric layers of down-sampling and up-sampling to solve depth-scene and 6-DoF estimation simultaneously. Also, we compared SpaceYNet to PoseNet - a state of the art in robot pose regression using CNN - in order to evaluate it. The comparison comprised one public dataset and one created in a broad indoor environment. SpaceYNet showed higher accuracy in global percentages when compared to PoseNet.
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Dataset, depth-scene, pose, regression, robot
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International Conference on Systems, Signals, and Image Processing, v. 2020-July, p. 217-222.