Learning-based camera pose estimation of images of an environment

摘要

A Deep Neural Network (DNN) system learns a map display to estimate a camera's position and orientation (pose). The DNN is trained to learn an environment-related map representation that defines positions and attributes of structures, trees, walls, vehicles, walls, etc. The DNN system learns a map display that is versatile and works well for many different environments (indoor, outdoor, natural, synthetic, etc.). The DNN system receives images of an environment captured by a camera (observations) and outputs an estimated camera pose within the environment. The estimated camera pose is used to perform camera localization, i. to regain the three-dimensional (3D) position and orientation of a moving camera, which is a fundamental task in computer vision with a wide variety of applications in robotic navigation, auto-localization for autonomous driving, device localization for mobile navigation and augmented / virtual reality.