Next Article in Journal
Lesson Learned from Collecting Quantified Self Information via Mobile and Wearable Devices
Previous Article in Journal
Performance Comparison of a Novel Adaptive Protocol with the Fixed Power Transmission in Wireless Sensor Networks
Open AccessArticle

On Optimal Multi-Sensor Network Configuration for 3D Registration

Computational Imaging and Visualization Analysis (CIVA) Lab, Department of Computer Science, University of Missouri-Columbia, Columbia 65211, MO, USA
Institute of Systems and Robotics, University of Coimbra, Faculty of Science and Technology, Coimbra 3000-315, Portugal
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
J. Sens. Actuator Netw. 2015, 4(4), 293-314;
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)
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
Show Figures

Graphical abstract

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.

Show more citation formats Show less citations formats

Article Access Map by Country/Region

Only visits after 24 November 2015 are recorded.
Back to TopTop