Latest revision as of 10:50, 23 March 2026

Abstract

This study addresses image matching in visual Simultaneous Localization and Mapping (SLAM) to enhance synchronous positioning and mapping using a multi-view stereo matching algorithm. Utilizing a vision odometer and a binocular camera, multi-view stereo images are captured. An improved ORB (Oriented FAST and Rotated BRIEF) algorithm extracts feature points and establishes binary descriptors, with similarity computed using Euclidean distance to enable stereo matching. Mismatches are filtered using parallax constraints and corrected through triangulation. Loopback detection minimizes cumulative drift in camera position, improving spatial perception. The back-end optimization employs graph optimization theory to refine the position and pose of the binocular camera and landmarks, addressing random noise and errors. Experimental results indicate effective feature extraction, with mismatches limited to three, overall positioning error under 100 mm, and directional error within 2°.OPEN ACCESS Received: 11/08/2025 Accepted: 14/01/2026

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