Description: a novel technique to align
partial 3D reconstructions of the seabed acquired by a stereo
camera mounted on an autonomous underwater vehicle. Vehicle
localization and seabed mapping is performed simultaneously
by means of an Extended Kalman Filter. Passive landmarks
are detected on the images and characterized considering 2D
and 3D features. Landmarks are re-observed while the robot
is navigating and data association becomes easier but robust.
Once the survey is completed, vehicle trajectory is smoothed by
a Rauch-Tung-Striebel filter obtaining an even better alignment
of the 3D views and yet a large-scale acquisition of the seabed.
- [Dualstimation] - Introduce a dual-estimated good article.
- [3D_SIFT_demo] - The MATLAB program using MATLAB to reali
- [ball_catcher] - This includes the project using a stereo
- [PDA] - Used to achieve single-target tracking s
- [Problem3] - stereo matching -sad -ssd -n
- [single-camera-slam] - Please read your package and describe
- [MCrecver] - Multicast receiving program can receive
- [SLAMTutorial2] - SLAM the latest research results and met
- [clouds] - ａmatalb file used to simulate the clouds
- [shark] - An underwater autonomous Vehicle (AUV) i
File list (Check if you may need any files):
多自由度运动空间kalman滤波\2008 IROS Visual SLAM for 3D Large-Scale Seabed Acquisition Employing Underwater Vehicles.pdf
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多自由度运动空间kalman滤波
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