## 2 Related Work
The traditional Hough Transform mainly detects straight lines, but rebar intersections require the precise localization of both crossing lines and corner points. To address this, researchers have developed an improved Hough Transform that can detect both corner points and line segments simultaneously. This variant introduces a new voting mechanism in the parameter space, enabling a more accurate identification of line segment endpoints and their intersections. By combining corner and line detection, the accuracy and efficiency of intersection recognition are significantly enhanced, providing reliable support for rebar tying automation [24]. However, the Hough Transform may struggle with noisy images. Noise can produce false edge points, leading to excessive meaningless votes in the Hough space and increasing the false detection rate. This also raises computational complexity and processing time [25]. Therefore, effective image preprocessing is essential before applying the Hough Transform to improve detection accuracy [26; 27]. In path planning and motion control, the Traveling Salesman Problem (TSP) is commonly used to optimize the robot's path among multiple rebar intersections. By planning the shortest route, the robot can visit each intersection sequentially, minimizing travel distance and time, thus improving overall tying efficiency. An improved A-based path planning method has also been proposed, combining priority constraints and dead zone escape algorithms to achieve the complete coverage of the work area and meet the requirements of rebar tying tasks. However, this approach still faces issues such as high computational complexity and poor adaptability to dynamic environments [28]. Another method utilizes Building Information Modeling (BIM) data for task planning in legged rebar tying robots. This approach generates initial intersection points from BIM, filters them based on the robot's operational constraints, and optimizes the task sequence using a constrained genetic algorithm. Experiments show that this method achieves an average tying efficiency of about 1500 points per hour, nearly twice that of manual work. Nevertheless, challenges remain in adapting to complex site conditions, managing hardware inconsistencies across robots, and scaling to large projects [29]. [PERSON] et al. proposed a novel motion and tying control strategy for rebar tying robots. The robot integrates multiple sensors, such as photoelectric sensors and limit switches, to ensure precise movement and operation within a confined rebar grid. With machine vision, the robot locates rebar nodes, adjusts the tying gun position, and ties multiple nodes sequentially. Experiments confirm that the system achieves a positioning accuracy within 5 mm and can tie four nodes at a single location. This design reduces unnecessary movement within the grid, enabling the efficient execution of repetitive tying tasks while maintaining quality standards [30]. To address the aforementioned issues, this paper proposes a robust image preprocessing method using a bilateral filter to reduce the impact of environmental noise on RGB-D images. The Hough Transform and line segment merging processes are further optimized to enhance the accuracy of intersection point detection. Additionally, a greedy serpentine path planning algorithm is introduced to improve operational efficiency. To increase the precision of intersection localization, a coordinate transformation approach based on coordinate system alignment is also presented. These improvements enable the robot to perform rebar tying tasks more efficiently and accurately.