Biomimetic Convergence-Based High Resolution Depth Estimation

Accurate depth estimation is a fundamental challenge in robotics and computer vision, crucial for applications ranging from autonomous navigation to complex object manipulation. While traditional techniques such as RGBD cameras and 3D Lidar systems provide high precision, their high costs and computational demands restrict their use in many scenarios. Conversely, monocular depth estimation (MDE) offers a more affordable solution but often struggles with depth accuracy and real-time performance. In response, this research introduces a novel convergence-based stereo vision system that leverages two independently movable cameras for dynamic depth estimation. The system facilitates depth tracking of any selected object by adapting camera orientation and extrinsic matrices to achieve millimeter-level accuracy with less than 1 % error in depth estimation over 5 meters, which is significantly better than few state-of-the-art Stereo systems that offer up to 2% error over a similar range. ArUco tag-based calibration mechanism was used to improve accuracy due to precise angle calculation. Further, the system successfully tracks and localizes multiple human body joints simultaneously using the Google Mediapipe Joint Detection model, providing a 3D approximation of the subject's position concerning the system in real-time. This approach not only offers a cost-effective and precise solution for real-time depth perception but also opens new avenues for dynamic, high-resolution environmental interaction in robotics.