FPGA Architectures for Real-time Dense SLAM

Abstract

Simultaneous Localization And Mapping (SLAM) is an important technique used in robotics, computer vision, and virtual/augmented reality. SLAM algorithms have moved past creating sparse maps to making dense 3D reconstruction of the environment. Dense SLAM algorithms have high computational demands that require hardware acceleration to be done efficiently in real-time. FPGAs are an attractive compute platform for SLAM systems as they are low power and high performance. Unfortunately, dense SLAM algorithms are complex and FPGAs are notoriously difficult to program. In this work, we study the best techniques for accelerating 3D reconstruction on FPGA. We analyze a 3D reconstruction system, and implement modular FPGA designs for the main components of this application. We target both an FPGA SoC and a larger FPGA PCIe board, and perform a design space exploration (DSE) of our designs. We analyze the results of our DSE, characterize the design spaces to highlight important features, and we implement the best designs in an open-source and end-to-end dense SLAM system running on a FPGA SoC board. On the SoC board, using the FPGA increases the throughput of the whole application by a factor of two compared to the ARM processor, and individual algorithms are up to 38 times faster on the FPGA.