A Real-Time and High Precision Hardware Implementation of RANSAC Algorithm for Visual SLAM Achieving Mismatched Feature Point Pair Elimination

The visual SLAM (vSLAM) algorithm is becoming a research hotspot in recent years because of its low cost and low delay. Due to the advantage of fitting irregular data input, random sample consensus (RANSAC) has become a commonly used method in vSLAM to eliminate mismatched feature point pairs in adjacent frames. However, the huge number of iterations and computational complexity of the algorithm make the hardware implementation and integration of the entire system challenging. This paper pioneeringly proposes an efficient hardware acceleration design with homography matrix as RANSAC hypothesis model, which achieves high speed and high precision. Through optimizing the direct linear transformation (DLT) method, the delay and resource consumption are reduced. The design is implemented on FPGA. Through the verification of Xilinx Zynq 7100 platform, the processing frame rate on EuRoc dataset is 709 fps, reaching an average speed up of 263.2x against ARM CPU, and a speed up of 1.2 similar to 50.0x compared with the advanced implementations in RANSAC part, which fully meets the real-time requirements. In addition, the root-mean-square error (RMSE) based on an open-source SLAM system (ICE-BA) on the EuRoc dataset reached 0.105 m, achieving an improvement of 15.6% in precision compared to the original ICE-BA system.