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- | ====== LSD-SLAM: Large-Scale Direct Monocular SLAM ====== | + | |
- | **Contact: | + | |
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- | **LSD-SLAM** is a novel, direct monocular SLAM technique: Instead of using keypoints, it directly operates on image intensities both for tracking and mapping. The camera is tracked using **direct image alignment**, | + | |
- | LSD-SLAM runs in **real-time on a CPU**, and even on a modern smartphone. | + | |
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- | As direct method, LSD-SLAM uses all information in the image, including e.g. edges -- while keypoint-based approaches can only use small patches around corners. | + | |
- | This leads to higher accuracy and more robustness in sparsely textured environments (e.g. indoors), and a much denser 3D reconstruction. Further, as the proposed piselwise depth-filters incorporate many small-baseline stereo comparisons instead of only few large-baseline frames, there are much less outliers. | + | |
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- | (click on the images for full resolution) | + | |
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- | LSD-SLAM builds a pose-graph of keyframes, each containing an estimated semi-dense depth map. Using a novel direct image alignment forumlation, | + | |
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- | The approach even runs on a smartphone, where it can be used for AR. The estimated semi-dense depth maps are in-painted and completed with an estimated ground-plane, | + | |
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- | LSD-SLAM is on github: | + | |
- | [[http:// | + | |
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- | We support only ROS-based build system tested on Ubuntu 12.04 or 14.04 and ROS Indigo or Fuerte. However, ROS is only used for input (video), output (pointcloud & poses) and parameter handling; ROS-dependent code is tightly wrapped and can easily be replaced. To avoid overhead from maintaining different build-systems however, we do not offer an out-of-the-box ROS-free version. Android-specific optimizations and AR integration are not part of the open-source release. | + | |
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- | Detailled installation and usage instructions can be found in the README.md, including descriptions of the most important parameters. For best results, we recommend using a monochrome [[http:// | + | |
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- | If you use our code, please cite our respective publications (see below). We are excited to see what you do with LSD-SLAM, if you want drop us a quick hint if you have nice videos / pictures / models / applications. | + | |
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- | To get you started, we provide some example sequences including the input video and camera calibration, | + | |
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- | Hint: Use '' | + | |
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- | * **Desk Sequence (0:55min, 640x480 @ 50fps)** | + | |
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- | * Video: [[http:// | + | |
- | * Pointcloud: [[http:// | + | |
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- | * **Machine Sequence (2:20min, 640x480 @ 50fps)** | + | |
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- | * Download Video: [[http:// | + | |
- | * Download Pointcloud: [[http:// | + | |
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- | * **Foodcourt Sequence (12min, 640x480 @ 50fps)** | + | |
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- | * Download Video: [[http:// | + | |
- | * Download Pointcloud: [[http:// | + | |
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- | * **ECCV Sequence (7:00min, 640x480 @ 50fps)** | + | |
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- | * **Enable FabMap for large loop-closures for this sequence!** | + | |
- | * Video: [[http:// | + | |
- | * Pointcloud: [[http:// | + | |
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- | LSD-SLAM is released under the GPLv3 license. We also offer a professional version under a different licensing agreement, intended for commercial use. Please contact us if you are interested. | + | |
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- | ====== Related publications ====== | + | |
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