Towards Energy-Optimized On-Board Computer Vision for Autonomous Underwater Vehicles
Daniel Gregorek, Sandesh Srinivas, Suhail Nasrulla, Steffen Paul, Ralf Bachmayer · OCEANS 2022, Hampton Roads · 2022
Autonomous underwater vehicles have increasing demands for computer vision capabilities. As an example, marine ecosystem observation will strongly benefit from real-time analysis of acquired images. However, the complexity of computer vision algorithms and the vast amount of data from still images or video induces serious challenges for the limited energy budget of the vehicles. We propose the extensive employment of field programmable gate array for the energy efficient implementation of on-board computer vision tasks. Our case study considers dedicated implementations for particle image velocimetry and stereo depth map estimation. The results show a substantial improvement in energy efficiency while maintaining attainable accuracy compared to a software reference model.