Hardware Accelerator for Feature Matching with Binary Search Tree

Abstract

Feature matching is an essential step for autonomous robot to localize itself during navigation. However, it is often difficult to achieve the matching in real-time due to limited on-board computing resources. We present an FPGA implementation for stream-processing based feature matching called Binary Search Tree Matcher (BSTM) that utilizes a balanced binary search tree. To improve the matching precision, we integrated a ratio-test (RT) outlier rejection mechanism in our design. Our proposed FPGA-based BSTM is scalable and resource efficient, and significantly faster than the best-performing hardware implementation in the literature. When compared to the commonly used Linear Exhaustive Search (LES) method running on the FPGA, our proposed design is approximately 12X faster.