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Tsubasa Goto (M2), Department of Precision Engineering, wins a Research Encouragement Award from the Japan Society for Precision Engineering

 

On 16th March 2018, Tsubasa Goto (M2), Department of Precision Engineering, wins a Research Encouragement Award from the Japan Society for Precision Engineering.

The prize of Research Encouragement Award related to the paper, which was published on Journal of the Japan Society for Precision Engineering in 2017, from the Japan Society for Precision Engineering.

 

 

<About awarded research>

Localization is an important task for mobile service robots in indoor spaces. In this research, we propose a novel technique for indoor localization using a spherical camera. Spherical cameras can obtain a complete view of the surroundings allowing the use of global environmental information. We take advantage of this in order to estimate camera position and the orientation with respect to a known 3D line map of an indoor environment, using a single image. We robustly extract 2D line information from the spherical image via spherical-gradient filtering and match it to 3D line information in the line map. Our method requires no information about the 3D-2D line correspondences. In order to avoid a complicated six degrees of freedom (6 DoF) search for position and orientation, we use a Manhattan world assumption to decompose the line information in the image. The 6 DoF localization process is divided into two phases. First, we estimate the orientation by extracting the three principle directions from the image. Then, the position is estimated by robustly matching the distribution of lines between the image and the 3D model via a spherical Hough representation. This decoupled search can robustly localize a spherical camera using a single image, as we demonstrate experimentally.

 

<Comments>

It is my pleasure to have this honorable award. I am thankful to my supervisors and co-authors of this research. Our research group intends to continue to develop the localization method, hoping that our results will contribute to actual sites.