  Algorithms based on Monte-Carlo sampling have been widely adapted in robotics
and other areas of engineering due to their performance robustness. However,
these sampling-based approaches have high computational requirements, making
them unsuitable for real-time applications with tight energy constraints. In
this paper, we investigate 6 degree-of-freedom (6DoF) pose estimation for robot
manipulation using this method, which uses rendering combined with sequential
Monte-Carlo sampling. While potentially very accurate, the significant
computational complexity of the algorithm makes it less attractive for mobile
robots, where runtime and energy consumption are tightly constrained. To
address these challenges, we develop a novel hardware implementation of
Monte-Carlo sampling on an FPGA with lower computational complexity and memory
usage, while achieving high parallelism and modularization. Our results show
12X-21X improvements in energy efficiency over low-power and high-end GPU
implementations, respectively. Moreover, we achieve real time performance
without compromising accuracy.
