Indoor Positioning System Based on Distributed Camera Sensor Networks for Mobile Robot
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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. In this paper, we propose a novel indoor positioning system (IPS) that uses calibrated camera sensors and 3D map information. 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 demonstrate that the proposed IPS based on the camera sensor network can provide accurate position information of moving objects.
KeywordsGlobal positioning system Indoor positioning system Camera network Mobile robot
This work was in part supported by Tough Robotics Challenge, ImPACT Program (Impulsing Paradigm Change through Disruptive Technologies Program).
- 2.Brščić, D., Hashimoto, H.: Model based robot localization using onboard and distributed laser range finders. In: Proceedings of the 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS2008), 1154–1159 (2008)Google Scholar
- 6.Dellaert, F., Fox, D., Burgard, W., Thrun, S.: Monte carlo localization for mobile robots. In: Proceedings of the 1999 IEEE International Conference on Robotics and Automation (ICRA1999), 1322–1328 (1999)Google Scholar
- 8.Ji, Y., Yamashita, A., Asama, H.: Automatic calibration and trajectory reconstruction of mobile robot in camera sensor network. In: Proceedings of the 11th Annual IEEE International Conference on Automation Science and Engineering (CASE2015), 206–211 (2015)Google Scholar
- 9.Ji, Y., Yamashita, A., Asama, H.: Automatic calibration of camera sensor networks based on 3D texture map information. Robot Auton Syst, (2016). doi: 10.1016/j.robot.2016.09.015
- 10.Konolige, K.: A gradient method for realtime robot control. In: Proceedings of the 2000 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS2000), 639–646 (2000)Google Scholar
- 11.Rahimi, A., Dunagan, B., Darrell, T.: Simultaneous calibration and tracking with a network of non-overlapping sensors. In: Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR2004), I–287 (2004)Google Scholar
- 13.Stauffer, C., Grimson, W.E.L.: Adaptive background mixture models for real-time tracking. In: Proceedings of the 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR1999), 246–252 (1999)Google Scholar