Simultaneous Localization and Mapping (SLAM) is a system used to achieve autonomous positioning and navigation. Feature detection is an important part of a SLAM system as fast and robust image matching is required for the task. A typical feature detection algorithm called Speeded-Up Robust Features (SURF) is used in a robot SLAM system with Moving Object Detection (MOD). This paper describes a modified feature detection algorithm based on Field Programmable Gate Array (FPGA) hardware. The paper focuses on implementing the software algorithm on a hardware platform. The advantage of the parallel and pipelining design of FPGA is fully applied to highly improve the performance and efficiency of the system. By using the FPGA hardware platform, the algorithm can also be implemented easily in an FPGA-based SLAM system afterward to finally use for System-On-Chip (SoC) applications.
Parallel and Pipelining design of SLAM Feature Detection Algorithm in Hardware Yunjie Liu