Vision-Based Detection, Localization, and Optimized Path Planning for Rebar Intersections in Automated Construction

Abstract

The accurate detection and precise spatial localization of rebar intersection points are essential for advancing automation in construction tasks, such as robotic rebar tying. This paper presents a vision-based methodology that integrates RGB-D sensing, camera calibration, and coordinate transformation techniques to robustly detect and localize rebar crossing points. A structured detection framework efficiently extracts intersection coordinates from RGB-D imagery, subsequently mapping these points to a global reference frame using extrinsic camera calibration parameters. To achieve comprehensive site coverage and optimize operational efficiency, the path planning challenge is reformulated as a sequencing optimization problem of the identified intersections. We propose a greedy optimization algorithm that generates smooth, snake-like traversal paths in an efficient manner. Experimental validation confirms the effectiveness of our approach, demonstrating detection accuracy exceeding 99%, an average processing time below 125 ms per intersection point, and a maximum coordinate transformation error under 2 mm. The presented solution offers a lightweight, precise, and scalable framework, significantly facilitating the integration of vision-based methods into automated construction workflows.