SpaceYNet: A Novel Approach to Pose and Depth-Scene Regression Simultaneously - Aragao, Dunfrey; Nascimento, Tiago; Mondini, Adriano [UNESP] (Institute of Electrical and Electronics Engineers (IEEE), 2020)
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.
Keywords: Dataset; depth-scene; pose; regression; robot