FPGA-based ORB Feature Extraction for Real-Time Visual SLAM
This work addresses real-time and energy efficiency challenges for SLAM on mobile or IoT devices, representing an incremental improvement in hardware acceleration.
The paper tackled the bottleneck of feature extraction in visual SLAM by designing an FPGA-based ORB feature extractor, achieving a balance between performance and energy consumption compared to ARM Krait and Intel Core i5 processors.
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.