FPGA-based path-planning of high mobility rover for future planetary missions

Work in navigational autonomy for planetary exploration rovers has focused on achieving safe and efficient path-planning through unknown, rocky terrain. Computer vision is playing an important role in increasing autonomy of both spacecraft and robotic vehicles, however due to their increased computational complexity, they are rarely implemented onto conventional computing systems. This problem becomes far more severe whenever real-time constraints have also to be considered. This paper addresses progress on rover path planning for high mobility mars rover missions. More specifically, at SPARTAN project, a demonstrator is being developed that meets ESA's specifications for the problem of rover Localization. This problem accounts for the generation of location estimates. This process involves four distinct applications: Imaging, 3D-Reconstruction, Visual Odometry (VO) and Visual Simultaneous Localization and Map reconstruction (SLAM). On supporting these functions we developed a novel HW/SW co-design methodology that extracts parallelism in higher algorithmic level, and thus allows the simultaneous execution of computational intensive kernels on a FPGA device, while the control flow is maintained by a low-performance CPU. Experimental results show that our implementation achieves the scopes of SPARTAN project.