  Simultaneous Localization And Mapping (SLAM) is the problem of constructing
or updating a map of an unknown environment while simultaneously keeping track
of an agent's location within it. How to enable SLAM robustly and durably on
mobile, or even IoT grade devices, is the main challenge faced by the industry
today. The main problems we need to address are: 1.) how to accelerate the SLAM
pipeline to meet real-time requirements; and 2.) how to reduce SLAM energy
consumption to extend battery life. After delving into the problem, we found
out that feature extraction is indeed the bottleneck of performance and energy
consumption. Hence, in this paper, we design, implement, and evaluate a
hardware ORB feature extractor and prove that our design is a great balance
between performance and energy consumption compared with ARM Krait and Intel
Core i5.
