Vision-Based Indoor Positioning System for Connected Vehicles in Small-scale Testbed Environments

In this paper, we present an innovative indoor positioning system designed for computing the position and orientation of multiple model-scale vehicles. Our system, equipped with four cameras, utilizes a novel image-stitching technique to stitch the images from these cameras into a single output image. We explore diverse methodologies for detecting and estimating the position and orientation of the model-scale vehicles, demonstrating robust detection within the plane. Through rigorous testing with vehicles of varying dimensions, our system achieves an accuracy ranging from approximately 0.5cm to 3cm on both axes. This establishes the system's potential applicability in scenarios involving model-scale autonomous vehicles, where precise knowledge of each vehicle's position is critical.