Pose estimation and 3D environment reconstruction using less reliable depth data

Sungjin Jo; Hyung Gi Jo; Hae Min Cho; Euntai Kim

doi:10.1109/AIM.2015.7222558

Pose estimation and 3D environment reconstruction using less reliable depth data

Sungjin Jo, Hyung Gi Jo, Hae Min Cho, Euntai Kim

Department of Electrical and Electronic Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

2 Citations (Scopus)

Abstract

Pose estimation and 3D reconstruction of environment are essential technics in robotics and computer vision. In this paper we present a method for camera tracking and 3D reconstruction of static environments, using a ToF sensor which provides less reliable depth information. Based on a primary camera pose, we eliminate outlier in distance measurements. Subsequently, we estimate camera pose again using only inlier data. A voxel grid map is updated by integrating depth measurement with a truncated signed distance function. It is represented as 3D environment reconstruction. Our method is an attractive extending of the pose estimation in outdoor environment. In outdoor environment, 3D range cameras cannot measure the distance or they provide inaccurate distance measurement. The experiments were carried out both in indoor and outdoor and we analyze the results of the proposed methods which use a ToF camera in comparison with a previous approach.

Original language	English
Title of host publication	AIM 2015 - 2015 IEEE/ASME International Conference on Advanced Intelligent Mechatronics
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	359-364
Number of pages	6
ISBN (Electronic)	9781467391078
DOIs	https://doi.org/10.1109/AIM.2015.7222558
Publication status	Published - 2015 Aug 25
Event	IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM 2015 - Busan, Korea, Republic of Duration: 2015 Jul 7 → 2015 Jul 11

Publication series

Name	IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM
Volume	2015-August

Other

Other	IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM 2015
Country/Territory	Korea, Republic of
City	Busan
Period	15/7/7 → 15/7/11

Bibliographical note

Publisher Copyright:
© 2015 IEEE.

All Science Journal Classification (ASJC) codes

Control and Systems Engineering
Software
Computer Science Applications
Electrical and Electronic Engineering

Access to Document

10.1109/AIM.2015.7222558

Cite this

Jo, S., Jo, H. G., Cho, H. M., & Kim, E. (2015). Pose estimation and 3D environment reconstruction using less reliable depth data. In AIM 2015 - 2015 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (pp. 359-364). Article 7222558 (IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM; Vol. 2015-August). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/AIM.2015.7222558

Jo, Sungjin ; Jo, Hyung Gi ; Cho, Hae Min et al. / Pose estimation and 3D environment reconstruction using less reliable depth data. AIM 2015 - 2015 IEEE/ASME International Conference on Advanced Intelligent Mechatronics. Institute of Electrical and Electronics Engineers Inc., 2015. pp. 359-364 (IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM).

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title = "Pose estimation and 3D environment reconstruction using less reliable depth data",

abstract = "Pose estimation and 3D reconstruction of environment are essential technics in robotics and computer vision. In this paper we present a method for camera tracking and 3D reconstruction of static environments, using a ToF sensor which provides less reliable depth information. Based on a primary camera pose, we eliminate outlier in distance measurements. Subsequently, we estimate camera pose again using only inlier data. A voxel grid map is updated by integrating depth measurement with a truncated signed distance function. It is represented as 3D environment reconstruction. Our method is an attractive extending of the pose estimation in outdoor environment. In outdoor environment, 3D range cameras cannot measure the distance or they provide inaccurate distance measurement. The experiments were carried out both in indoor and outdoor and we analyze the results of the proposed methods which use a ToF camera in comparison with a previous approach.",

author = "Sungjin Jo and Jo, {Hyung Gi} and Cho, {Hae Min} and Euntai Kim",

note = "Publisher Copyright: {\textcopyright} 2015 IEEE.; IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM 2015 ; Conference date: 07-07-2015 Through 11-07-2015",

year = "2015",

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doi = "10.1109/AIM.2015.7222558",

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series = "IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM",

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Jo, S, Jo, HG, Cho, HM & Kim, E 2015, Pose estimation and 3D environment reconstruction using less reliable depth data. in AIM 2015 - 2015 IEEE/ASME International Conference on Advanced Intelligent Mechatronics., 7222558, IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM, vol. 2015-August, Institute of Electrical and Electronics Engineers Inc., pp. 359-364, IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM 2015, Busan, Korea, Republic of, 15/7/7. https://doi.org/10.1109/AIM.2015.7222558

Pose estimation and 3D environment reconstruction using less reliable depth data. / Jo, Sungjin; Jo, Hyung Gi; Cho, Hae Min et al.
AIM 2015 - 2015 IEEE/ASME International Conference on Advanced Intelligent Mechatronics. Institute of Electrical and Electronics Engineers Inc., 2015. p. 359-364 7222558 (IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM; Vol. 2015-August).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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AU - Jo, Hyung Gi

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N2 - Pose estimation and 3D reconstruction of environment are essential technics in robotics and computer vision. In this paper we present a method for camera tracking and 3D reconstruction of static environments, using a ToF sensor which provides less reliable depth information. Based on a primary camera pose, we eliminate outlier in distance measurements. Subsequently, we estimate camera pose again using only inlier data. A voxel grid map is updated by integrating depth measurement with a truncated signed distance function. It is represented as 3D environment reconstruction. Our method is an attractive extending of the pose estimation in outdoor environment. In outdoor environment, 3D range cameras cannot measure the distance or they provide inaccurate distance measurement. The experiments were carried out both in indoor and outdoor and we analyze the results of the proposed methods which use a ToF camera in comparison with a previous approach.

AB - Pose estimation and 3D reconstruction of environment are essential technics in robotics and computer vision. In this paper we present a method for camera tracking and 3D reconstruction of static environments, using a ToF sensor which provides less reliable depth information. Based on a primary camera pose, we eliminate outlier in distance measurements. Subsequently, we estimate camera pose again using only inlier data. A voxel grid map is updated by integrating depth measurement with a truncated signed distance function. It is represented as 3D environment reconstruction. Our method is an attractive extending of the pose estimation in outdoor environment. In outdoor environment, 3D range cameras cannot measure the distance or they provide inaccurate distance measurement. The experiments were carried out both in indoor and outdoor and we analyze the results of the proposed methods which use a ToF camera in comparison with a previous approach.

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Jo S, Jo HG, Cho HM, Kim E. Pose estimation and 3D environment reconstruction using less reliable depth data. In AIM 2015 - 2015 IEEE/ASME International Conference on Advanced Intelligent Mechatronics. Institute of Electrical and Electronics Engineers Inc. 2015. p. 359-364. 7222558. (IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM). doi: 10.1109/AIM.2015.7222558

Pose estimation and 3D environment reconstruction using less reliable depth data

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