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

TL;DR

PoseNet introduces a real-time convolutional neural network that accurately estimates 6-DOF camera pose from a single RGB image, operating efficiently indoors and outdoors without complex optimization.

Contribution

The paper presents a novel end-to-end CNN approach for 6-DOF relocalization that is fast, robust, and requires minimal training data, outperforming traditional methods in challenging conditions.

Findings

Achieves 2m and 6° accuracy outdoors, 0.5m and 10° indoors

Operates in real time at 5ms per frame

Robust to lighting, motion blur, and camera variations

Abstract

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