Future autonomous spacecraft rendezvous with uncooperative or unprepared objects will be enabled by vision-based navigation, which imposes great computational challenges. Targeting short duration missions in low Earth orbit, this paper develops high-performance avionics supporting custom computer vision algorithms of increased complexity for satellite pose tracking. At algorithmic level, we track 6D pose by rendering a depth image from an object mesh model and robustly matching edges detected in the depth and intensity images. At system level, we devise an architecture to exploit the structure of commercial system-on-chip FPGAs, i.e., Zynq7000, and the benefits of tightly coupling VHDL accelerators with CPU-based functions. At implementation level, we employ our custom HW/SW co-design methodology and an elaborate combination of digital circuit design techniques to optimize and map efficiently all functions to a compact embedded device. Providing significant performance per watt improvement, the resulting VBN system achieves a throughput of 10-14 FPS for 1 Mpixel images, with only 4.3 watts mean power and 1U size, while tracking ENVISAT in real-time with only 0.5% mean positional error.