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

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

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.

Original languageEnglish
Title of host publication2022 IEEE/SICE International Symposium on System Integration, SII 2022
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages401-407
Number of pages7
ISBN (Electronic)9781665445405
DOIs
Publication statusPublished - 2022
Event2022 IEEE/SICE International Symposium on System Integration, SII 2022 - Virtual, Narvik, Norway
Duration: 9 Jan 202212 Jan 2022

Publication series

Name2022 IEEE/SICE International Symposium on System Integration, SII 2022

Conference

Conference2022 IEEE/SICE International Symposium on System Integration, SII 2022
Country/TerritoryNorway
CityVirtual, Narvik
Period9/01/2212/01/22

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