High Performance Computing for Autonomous Planetary Exploration

Modern parallel computers could power the perception and compression algorithms small planetary rovers require to navigate long distances, construct detailed terrain maps, and communicate discoveries to Earth. This work identifies and comprehensively characterizes four algorithms important to planetary roving that are well-suited for parallel computing. Multiple implementations of dense stereo matching, multi-view stereo, image compression, and triangle mesh compression are evaluated using the NVIDIA Jetson family of high-performance embedded computers. Image and mesh inputs are derived from simulation and used to evaluate the performance, power consumption, and hardware utilization of each device as a function of time. Our results demonstrate the promising capacity for modern embedded computers to expand the range and pace of planetary rover exploration.