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We present a robust and real-time monocular six degree of freedomrelocalization system. Our system trains a convolutional neural network toregress the 6-DOF camera pose from a single RGB image in an end-to-end mannerwith no need of additional engineering or graph optimisation. The algorithm canoperate indoors and outdoors in real time, taking 5ms per frame to compute. Itobtains approximately 2m and 6 degree accuracy for large scale outdoor scenesand 0.5m and 10 degree accuracy indoors. This is achieved using an efficient 23layer deep convnet, demonstrating that convnets can be used to solvecomplicated out of image plane regression problems. This was made possible byleveraging transfer learning from large scale classification data. We show theconvnet localizes from high level features and is robust to difficult lighting,motion blur and different camera intrinsics where point based SIFT registrationfails. Furthermore we show how the pose feature that is produced generalizes toother scenes allowing us to regress pose with only a few dozen trainingexamples. PoseNet code, dataset and an online demonstration is available on ourproject webpage, at http://mi.eng.cam.ac.uk/projects/relocalisation/

PoseNet: A Convolutional Network for Real-Time 6-DOF Camera Relocalization