samarth-robo/MapNet
CVPR 2018 Spotlight: MapNet: Geometry-Aware Learning of Maps for Camera Localization
This project helps roboticists and autonomous vehicle developers accurately determine a camera's position and orientation within a known environment by analyzing a stream of images. It takes video footage of a scene as input and outputs the precise location (pose) of the camera. This is ideal for researchers and engineers working on navigation and localization systems for robots or self-driving cars.
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Use this if you need to precisely track a camera's location in a familiar setting and want to improve accuracy using additional unlabeled video data.
Not ideal if you're working with completely new, unseen environments where no prior 'map' or training data exists.
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May 18, 2018
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