RT-FLOW: FPGA Implementation of Real-Time Optical-Flow-Based SLAM for High-Speed Tracking and High-Quality Mapping

Simultaneous Localization and Mapping (SLAM) is pivotal for autonomous robotics, yet feature-based SLAM systems struggle with sparse environmental representations and robustness under dynamic conditions. Optical-flow-based SLAM (OpF-SLAM) addresses these limitations by leveraging pixel-level motion data for dense mapping; however, its computational intensity hinders real-time deployment. This paper presents RT-FLOW, an FPGA-based accelerator for OpF-SLAM that achieves real-time performance through three key innovations: 1) A feature-context encoding engine that exploits inter-frame similarity to resolve data dependency in correlation construction, reducing latency by 77.5%. 2) A heterogeneous mixed-precision flow update engine guided by correlation sparsity, enabling 3.7× faster optical flow computation with negligible accuracy loss. 3) A pivoting-free linear solver using Householder transformations for stable pose optimization. Implemented on Xilinx XCZU7EV FPGA, RT-FLOW processes full-image pixels per frame at 65 fps with an energy efficiency of 0.358 μJ/point, outperforming previous FPGA designs. Evaluated on benchmark datasets, RT-FLOW demonstrates robustness in diverse environments while maintaining sub-110mJ/frame energy consumption. This work bridges the gap between algorithmic potential and hardware feasibility for high-density SLAM, empowering next-generation mobile robots with real-time scene understanding capabilities.