Collaborative Multi-Sensor Image Transmission and Data Fusion in Mobile Visual Sensor Networks Equipped with RGB-D Cameras

Xiaoqin Wang (1) , Ahmet Sekercioglu (1) , Tom Drummond (1) , Enrico Natalizio (2) , Isabelle Fantoni (2) , Vincent Fremont (2)

1 Monash University [Clayton]
2 Heudiasyc - Heuristique et Diagnostic des Systèmes Complexes [Compiègne]

We present a scheme for multi-sensor data fusion applications, called Relative Pose based Redundancy Removal (RPRR), that efficiently enhances the wireless channel utilization in bandwidth-constrained operational scenarios for RGBD camera equipped visual sensor networks. Pairs of nodes cooperatively determine their own relative pose, and by using this knowledge they identify the correlated data related to the common regions of the captured color and depth images. Then, they only transmit the non-redundant information present in these images. As an additional benefit, the scheme also extends the battery life through reduced number of packet transmissions. Experimental results confirm that significant gains in terms of wireless channel utilization and energy consumption would be achieved when the RPRR scheme is used in visual sensor network operations.

Domaines: Vision par ordinateur et reconnaissance de formes [cs.CV]; Robotique [cs.RO]

Xiaoqin Wang, Ahmet Sekercioglu, Tom Drummond, Enrico Natalizio, Isabelle Fantoni, et al.. Collaborative Multi-Sensor Image Transmission and Data Fusion in Mobile Visual Sensor Networks Equipped with RGB-D Cameras. IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2016), Sep 2016, Baden-Baden, Germany. pp.1-8. ⟨hal-01398316⟩