Using a Bayesian convolutional neural network implementation we obtain an estimate of the model's relocalization uncertainty and improve state of the art localization accuracy on a large scale outdoor dataset.
We present a robust and real-time monocular six degree of freedom relocalization system.
Temporal camera relocalization estimates the pose with respect to each video frame in sequence, as opposed to one-shot relocalization which focuses on a still image.
Camera relocalization plays a vital role in many robotics and computer vision tasks, such as global localization, recovery from tracking failure, and loop closure detection.
CAMERA RELOCALIZATION LOOP CLOSURE DETECTION SIMULTANEOUS LOCALIZATION AND MAPPING