Image feature-based SLAM for flat surface modeling in indoor environment

Tamaki Kobayashi, Masafumi Nakagawa

研究成果: Paper査読

抄録

In this study, we focused on image features in point cloud data to improve robustness of simultaneous localization and mapping (SLAM) for indoor mapping. We also focused on 3D area scanner to acquire point cloud data with reflection intensity images for image feature-based SLAM. Our methodology consists of five steps. First, point cloud data are acquired using a time-of-flight (TOF) camera from continuous viewpoints. Second, intensity images are generated from point cloud data. Third, the feature points are estimated from reflection intensity images with feature descriptors. Fourth, camera rotation matrices are estimated using corresponded feature points in intensity images. Finally, acquired point cloud data are registered using the estimated rotation matrices. We clarified that our methodology can integrate point cloud data successfully through our experiments in indoor environments.

本文言語English
出版ステータスPublished - 2017
イベント38th Asian Conference on Remote Sensing - Space Applications: Touching Human Lives, ACRS 2017 - New Delhi, India
継続期間: 2017 10 232017 10 27

Other

Other38th Asian Conference on Remote Sensing - Space Applications: Touching Human Lives, ACRS 2017
CountryIndia
CityNew Delhi
Period17/10/2317/10/27

ASJC Scopus subject areas

  • Computer Networks and Communications

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