FSLAM: an Efficient and Accurate SLAM Accelerator on SoC FPGAs

Abstract

Simultaneous Localization and Mapping (SLAM) is one of the main components of autonomous navigation systems. With the increase in popularity of drones, autonomous navigation on low-power systems is seeing widespread application. Most SLAM algorithms are computationally intensive and struggle to run in real-time on embedded devices with reasonable accu-racy. ORB-SLAM is an open-sourced feature-based SLAM that achieves high accuracy with reduced computational complexity. We propose an FPGA based ORB-SLAM system, named FSLAM, that accelerates the computationally intensive visual feature extraction and matching on hardware. FSLAM is based on a Zynq-family SoC and runs 8.5x, 1.55x and 1.35x faster compared to an ARM CPU, Intel Desktop CPU, and a state-of-the-art FPGA system respectively, while averaging a 2x improvement in accuracy compared to prior work on FPGA.