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research:semidense [2013/11/18 16:47] engelj |
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- | ====== (Semi-)Dense SLAM for Monocular Cameras ====== | + | |
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- | Contact: [[members: | + | |
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- | In the last years - in particular with the advent of commodity depth cameras - **dense methods** for camera tracking and 3d-reconstruction have quickly gained popularity. Instead of abstracting the images to a set of feature observations (such as corners, blobs or line-segments), | + | |
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- | We aim at developing similar methods for ordinary **monocular cameras**, where no direct depth measurements are available. | + | |
- | While the absence of a direct depth measuring means makes the problem more complex, it also has important benefits: | + | |
- | * The inherent **scale-ambivalence** allows to use image regions at any depth (the range of RGB-D cameras is very limited). Especially outdoors, where typically a large portion of the scene is far away, this is very beneficial. | + | |
- | * A monocular camera is significantly **smaller, lighter and cheaper** than stereo- or in RGB-D camera, and hence more suited as sensor for mobile or robotic applications. | + | |
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- | ====== Semi-Dense Visual Odometry ====== | + | |
- | The proposed **Semi-Dense** visual odometry / SLAM method **uses all image-regions which carry information**, | + | |
- | In addition to highly accurate camera odometry, it provides accurate, semi-dense depth maps of the environment, | + | |
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- | ====== Related publications ====== | + | |
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