Pose Error

Pose error, the inaccuracy in estimating the position and orientation of objects or humans, is a significant challenge across various computer vision applications. Current research focuses on mitigating pose error through improved sensor fusion techniques (e.g., combining visual and inertial data), robust algorithms like Iterative Closest Point (ICP) that are less susceptible to noisy inputs, and deep learning models designed to tolerate unreliable tracking data. Addressing pose error is crucial for advancing applications such as autonomous navigation, human-computer interaction, and group activity recognition, where accurate pose estimation is essential for reliable system performance.