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J. Sens. Actuator Netw. 2015, 4(4), 293-314; doi:10.3390/jsan4040293

On Optimal Multi-Sensor Network Configuration for 3D Registration

1
Computational Imaging and Visualization Analysis (CIVA) Lab, Department of Computer Science, University of Missouri-Columbia, Columbia 65211, MO, USA
2
Institute of Systems and Robotics, University of Coimbra, Faculty of Science and Technology, Coimbra 3000-315, Portugal
3
Robotics Institute, Khalifa University of Science, Technology & Research, Abu Dhabi 127788, United Arab Emirates
*
Author to whom correspondence should be addressed.
Academic Editor: Miao Jin
Received: 25 July 2015 / Revised: 25 October 2015 / Accepted: 30 October 2015 / Published: 4 November 2015
(This article belongs to the Special Issue 3D Wireless Sensor Network)
View Full-Text   |   Download PDF [6499 KB, uploaded 6 November 2015]   |  

Abstract

Multi-sensor networks provide complementary information for various taskslike object detection, movement analysis and tracking. One of the important ingredientsfor efficient multi-sensor network actualization is the optimal configuration of sensors.In this work, we consider the problem of optimal configuration of a network of coupledcamera-inertial sensors for 3D data registration and reconstruction to determine humanmovement analysis. For this purpose, we utilize a genetic algorithm (GA) based optimizationwhich involves geometric visibility constraints. Our approach obtains optimal configurationmaximizing visibility in smart sensor networks, and we provide a systematic study usingedge visibility criteria, a GA for optimal placement, and extension from 2D to 3D.Experimental results on both simulated data and real camera-inertial fused data indicate weobtain promising results. The method is scalable and can also be applied to other smartnetwork of sensors. We provide an application in distributed coupled video-inertial sensorbased 3D reconstruction for human movement analysis in real time. View Full-Text
Keywords: optimal configuration; sensor network; genetic algorithm; 3D; reconstruction;registration; human movements optimal configuration; sensor network; genetic algorithm; 3D; reconstruction;registration; human movements
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Aliakbarpour, H.; Prasath, V.B.S.; Dias, J. On Optimal Multi-Sensor Network Configuration for 3D Registration. J. Sens. Actuator Netw. 2015, 4, 293-314.

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