Visual Odometry Offloading in Internet of Vehicles with Compression at the Edge of the Network

Li Qingqing, Jorge Peña Queralta, Tuan Nguyen Gia, Hannu Tenhunen, Zhou Zou and Tomi Westerlund

A recent trend in the IoT is to shift from traditional cloud centric applications towards more distributed approaches embracing the fog and edge computing paradigms. In autonomous robots and vehicles, much research has been put into the potential of offloading computationally intensive tasks to cloud computing. Visual odometry is a common example, as real-time analysis of one or multiple video feeds requires significant on-board computation. If this operations are offloaded, then the on-board hardware can be simplified, and the battery life extended. In the case of self-driving cars, efficient offloading can significantly decrease the price of the hardware. Nonetheless, offloading to cloud computing compromises the system’s latencyand poses serious reliability issues. Visual odometry offloading requires streaming of video-feeds in real-time. In a multi-vehicle scenario, enabling efficient data compression without compromising performance can help save bandwidth and increase reliability.