  Simultaneous Localization and Mapping (SLAM) is a critical task for
autonomous navigation. However, due to the computational complexity of SLAM
algorithms, it is very difficult to achieve real-time implementation on
low-power platforms.We propose an energy efficient architecture for real-time
ORB (Oriented-FAST and Rotated- BRIEF) based visual SLAM system by accelerating
the most time consuming stages of feature extraction and matching on FPGA
platform.Moreover, the original ORB descriptor pattern is reformed as a
rotational symmetric manner which is much more hardware friendly. Optimizations
including rescheduling and parallelizing are further utilized to improve the
throughput and reduce the memory footprint. Compared with Intel i7 and ARM
Cortex-A9 CPUs on TUM dataset, our FPGA realization achieves up to 3X and 31X
frame rate improvement, as well as up to 71X and 25X energy efficiency
improvement, respectively.
