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Construction of Intelligent Space [ April 2014 ~ May 2019 ]

Research Background
Automatic Calibration of Camera Sensor Network
Figure 1 (a) illustrates an example of the map information which is built by typical simultaneous localization and mapping (SLAM) schemes. However, considering human-robot coexistence environments, the map information, which is a static model, cannot deal with such dynamic environments because it cannot reflect changes in the environment (e.g., moving objects, etc.). On the other hand, the concept of an intelligent space, as illustrated in Fig. 1 (b), which constructs a distributed sensor network in an external environment, can monitor what is occurring in it.

Distributed sensor networks installed in external environments can recognize various events that occur in the space, so that such intelligent space can be of much service in human–robot coexistence environments, as shown in Fig. 1 (b). Distributed camera sensor networks with multi-camera systems provide the most general infrastructure for constructing such intelligent space. In order to obtain reliable information from such a system, pre-calibration of all the cameras in the environment (i.e., determining the absolute positions and orientations of each camera) is an essential task that is extremely tedious. This research considers the automatic calibration method for camera sensor networks based on 3D texture map information of a given environment as shown in Fig. 1(a). In other words, this research solves a global localization problem for the poses of the camera sensor networks given the 3D texture map information. The proposed complete 6DOF calibration system in this research only uses the environment map information; therefore, the proposed scheme easily calibrates its parameters. The results shown in Mov. 1 demonstrate that the proposed system can calibrate complete external camera parameters successfully.

Fig. 1 Environmental information: (a) static information from map and (b) dynamic information from sensor network which is components of intelligent space.


Mov. 1 Experimental results of automatic calibration of camera sensor network using wireless IP camera.

Indoor Positioning System Based on Distributed Camera Sensor Network
An importance of accurate position estimation in the field of mobile robot navigation cannot be overemphasized. In case of an outdoor environment, a global positioning system (GPS) is widely used to measure the position of moving objects. However, the satellite based GPS does not work indoors. This research proposes an indoor positioning system (IPS) that uses calibrated camera sensor networks for mobile robot navigation.

The IPS information is obtained by generating a bird's-eye image from multiple camera images; thus, our proposed IPS can provide accurate position information when the moving object is detected from multiple camera views. We evaluate the proposed IPS in a real environment in a wireless camera sensor network. The results shown in Mov. 2 demonstrate that the proposed IPS based on the camera sensor network can provide accurate position information of moving objects.


Mov. 2 Experimental results of IPS for mobile robot localization.

Related Paper
Yonghoon Ji, Atsushi Yamashita, Kazunori Umeda, and Hajime Asama, "Automatic Camera Pose Estimation Based on a Flat Surface Map," Proceedings of the SPIE 11172, 14th International Conference on Quality Control by Artificial Vision (QCAV2019), Vol. 11172, pp. 111720X-1-111720X-6, Mulhouse, France, May 2019. [Link]
池 勇勳, 山下 淳, 梅田 和昇, 淺間 一, "人工物環境における直線情報を用いたカメラの外部パラメータ推定法," 第19回計測自動制御学会システムインテグレーション部門講演会講演論文集 (SI2018), 3B3-17, pp. 2598-2600, 大阪, December 2018. (SI2018優秀講演賞受賞)
Yonghoon Ji, Atsushi Yamashita, and Hajime Asama, "Automatic Calibration of Camera Sensor Network Based on 3D Texture Map Information," Robotics and Autonomous Systems, Vol. 87, pp. 313-328, ISSN 0921-8890, January 2017 (Online: October 5 2016). [doi:10.1016/j.robot.2016.09.015] (Impact Factor 2.928)
Yonghoon Ji, Atsushi Yamashita, and Hajime Asama, "Indoor Positioning System Based on Distributed Camera Sensor Networks for Mobile Robot," Advances in Intelligent Systems and Computing 531, Intelligent Autonomous Systems 14 (Weidong Chen, Koh Hosoda, Emanuele Menegatti, Masahiro Shimizu and Hesheng Wang (Eds.)) (Proceedings of the 14th International Conference IAS-14, Held July 2016, Shanghai (China)), Springer, pp. 1089-1101, ISSN. 2194-5357, February 2017 (Online: eISSN. 2194-5365). [doi:10.1007/978-3-319-48036-7]
지 용훈, Atsushi Yamashita, Hajime Asama, "실내 환경에서의 이동로봇의 위치추정을 위한 카메라 센서 네트워크 기반의 실내 위치 확인 시스템," 제어・로봇・시스템학회 논문지, Vol. 22, No. 11, pp. 952-959, ISSN 1976-5622, November 2016 (Online: eISSN 2233-4335). [Link]
지 용훈, Atsushi Yamashita, and Hajime Asama, "카메라 네트워크를 활용한 3 차원 지도정보 기반의 실내 위치 확인 시스템," 2016 제31회 제어・로봇・시스템학회 학술대회, pp. 1-2, 서울, March 2016.
Yonghoon Ji, Atsushi Yamashita, and Hajime Asama, "Automatic Camera Pose Estimation Based on Textured 3D Map Information," Proceedings of the 2015 JSME/RMD International Conference on Advanced Mechatronics (ICAM2015), pp.100-101, Tokyo, Japan, December 2015. [Link] (ICAM2015 Honorable Mention)
Yonghoon Ji, Atsushi Yamashita, and Hajime Asama, "Automatic Calibration and Trajectory Reconstruction of Mobile Robot in Camera Sensor Network," Proceedings of the 11th Annual IEEE International Conference on Automation Science and Engineering (CASE2015), pp. 206-211, Gothenburg, Sweden, August 2015. [Link]
池 勇勳, 山下 淳, 淺間 一, "移動ロボットによるカメラネットワークの自動キャリブレーション-知能化空間における地図情報による性能向上-," 日本機械学会ロボティクス・メカトロニクス講演会15講演論文集 (ROBOMECH2015), 2A1-P06, pp. 1-2, 京都, May 2015.
池 勇勳, 山下 淳, 淺間 一, "知能化空間での移動ロボットによる自己位置推定と自動カメラキャリブレーションの同時実行," 第20回ロボティクスシンポジア講演予稿集, pp. 172-177, 軽井沢, March 2015.
池 勇勳, 山下 淳, 淺間 一, "環境知能化による移動ロボットのモンテカルロ位置推定法の性能向上," 第32回日本ロボット学会画学術講演会予稿集 (RSJ2014), RSJ2014AC3J1-06, pp. 1-4, 福岡, September 2014.