研究SLAM网站和有用站点-sourcecode+测试数据

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1--->http://openslam.org/

The simultaneous localization and mapping (SLAM) problem has been intensively studied in the robotics community in the past. Different techniques have been proposed but only a few of them are available as implementations to the community. The goal of OpenSLAM.org is to provide a platform for SLAM researchers which gives them the possibility to publish their algorithms. OpenSLAM.org provides to every interested SLAM researcher a subversion (svn) repository and a small webpage in order to publish and promote their work. In the repository, only the authors have full access to the files; other users are restricted to read-only access. OpenSLAM.org does not really aim to provide a repository for the daily development process of early SLAM implementations. Published algorithm should have a certain degree of robustness.

OpenSLAM.org does not force the authors to give away the copyright for their code. We only require that the algorithms are provided as source code and that the authors allow the users to use and modify the source code for their own research. Any commercial application, redistribution, etc has to be arranged between users and authors individually.

2--->http://cvpr.in.tum.de/data/datasets/rgbd-dataset

RGB-D SLAM Dataset and Benchmark

Contact: Jürgen Sturm

We provide a large dataset containing RGB-D data and ground-truth data with the goal to establish a novel benchmark for the evaluation of visual odometry and visual SLAM systems. Our dataset contains the color and depth images of a Microsoft Kinect sensor along the ground-truth trajectory of the sensor. The data was recorded at full frame rate (30 Hz) and sensor resolution (640×480). The ground-truth trajectory was obtained from a high-accuracy motion-capture system with eight high-speed tracking cameras (100 Hz). Further, we provide the accelerometer data from the Kinect. Finally, we propose an evaluation criterion for measuring the quality of the estimated camera trajectory of visual SLAM systems.

3--->http://vision.ia.ac.cn/Students/gzp/monocularslam.html

Monocular SLAM

The research in monocular SLAM technology is mainly based on the EKF(Extended Kalman Filter) SLAM approaches.

4---> http://www.cvpapers.com/rr.html

Computer Vision Algorithm Implementations

5---->http://www.mrpt.org/

the mobile robot programming toolkit非常好的东西

6----> 自己研究方向的andrew j. Davsion

http://www.doc.ic.ac.uk/~ajd/index.html

基于单目视觉的SLAM实现与研究

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