Cooperative Positioning

Cooperative positioning leverages inter-agent communication to enhance the accuracy and robustness of localization, particularly in challenging environments where individual positioning systems are unreliable. Current research focuses on optimizing network geometry and incorporating constraints (e.g., road planes, stop lines) into estimation algorithms, often employing techniques like factor graph optimization, Kalman filtering, and reinforcement learning to fuse data from various sources (GNSS, inter-vehicle ranging, visual data). This field is significant for improving the performance of autonomous vehicles, robotic networks, and even sports performance analysis, offering more precise and reliable positioning in scenarios where traditional methods fall short.