Robot Pose

Robot pose estimation, the process of determining a robot's position and orientation, is crucial for autonomous navigation, manipulation, and human-robot interaction. Current research emphasizes robust methods for pose estimation under challenging conditions, such as partial visibility, noisy sensor data, and unknown robot states, employing techniques like deep neural networks, Gaussian belief propagation, and optimization-based approaches including those leveraging visual SLAM and keypoint detection. These advancements are vital for improving the reliability and safety of robots in diverse real-world applications, ranging from industrial automation and collaborative robotics to space exploration and assistive technologies.