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Home Research Areas Semi-Dense Monocular Visual Odometry

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research:semidense [2014/07/02 08:47]
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research:semidense [2014/07/02 08:51] (current)
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-====== LSD-SLAM: Large-Scale Direct Monocular SLAM ====== +
-{{:​research:​semidense:​semidenseexamples.png?​500|}} +
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-Contact: [[members:​engelj|Jakob Engel]] +
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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),​ they direcly operate on the raw images. This allows to exploit all information in the image and achieve high-accuracy tracking and reconstruction results in real-time. +
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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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-<html><​center></​html>​{{:​research:semidense:​semidensevskeypoints.png?​direct&​640|}}<​html></​center></​html>​ +
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-====== Semi-Dense Visual Odometry ====== +
-The proposed **Semi-Dense** visual odometry / SLAM method **uses all image-regions which carry information**,​ i.e., which have a non-negligible intensity gradient. In contrast to keypoint-based methods this includes regions with gradient in only one direction (edges). The statistical depth map representation and the omission of intensity-uniform image regions allows it to run in **real-time on a CPU**. +
-In addition to highly accurate camera odometry, it provides accurate, semi-dense depth maps of the environment,​ which can be valuable e.g. for robot navigation. +
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-<​html><​center><​iframe width="​640"​ height="​440"​ src="//​www.youtube.com/​embed/​LZChzEcLNzI"​ frameborder="​0"​ allowfullscreen></​iframe></​center></​html>​ +
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-====== Related publications ====== +
-<​bibtex>​ +
-<​keywords>​semidense</​keywords>​ +
-</​bibtex>​+

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