Camera Attitude Estimation by Neural Network Using Classification Network Method Instead of Numerical Regression

Hibiki Kawai, Wataru Yoshiuchi, Yasunori Hirakawa, Takumi Shibuya, Takumi Matsuda, Yoji Kuroda

研究成果: Conference contribution査読

抄録

In this paper, we propose a method for estimating the camera pose using a classification neural network based on the idea of OCR. Some terrestrial robots can exhibit high maneuverability by freely tilting their upper bodies. When estimating the posture of such robots, posture estimation using IMU and gyroscopic sensors as in the case of drones is affected by the noise generated by the unevenness of the ground, making posture estimation difficult. Pose estimation using deep learning from camera images is one solution to these problems, and various studies have been conducted in the past. However, the accuracy of pose estimation using only inference by deep learning with camera images is extremely poor and is not practical. In order to solve this problem, this paper proposes a classification neural network based on the idea of OCR, which can ensure high inference accuracy in the pose estimation task.

本文言語English
ホスト出版物のタイトル2022 IEEE/SICE International Symposium on System Integration, SII 2022
出版社Institute of Electrical and Electronics Engineers Inc.
ページ401-407
ページ数7
ISBN(電子版)9781665445405
DOI
出版ステータスPublished - 2022
イベント2022 IEEE/SICE International Symposium on System Integration, SII 2022 - Virtual, Narvik, Norway
継続期間: 9 1月 202212 1月 2022

出版物シリーズ

名前2022 IEEE/SICE International Symposium on System Integration, SII 2022

Conference

Conference2022 IEEE/SICE International Symposium on System Integration, SII 2022
国/地域Norway
CityVirtual, Narvik
Period9/01/2212/01/22

フィンガープリント

「Camera Attitude Estimation by Neural Network Using Classification Network Method Instead of Numerical Regression」の研究トピックを掘り下げます。これらがまとまってユニークなフィンガープリントを構成します。

引用スタイル