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cs:slam:start [2019/05/01 14:56] Steve Hemm [Tracking] |
cs:slam:start [2019/08/18 16:36] (current) Steve Hemm [Overview] |
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| - | ====== SLAM (Page still under construction) ====== | + | ====== SLAM ====== |
| ====== Simultaneous Localization and Mapping ====== | ====== Simultaneous Localization and Mapping ====== | ||
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| In robot navigation, a SLAM algorithm is used to construct a map of the robot's environment, while simultaneously locating the robot within that map. There are many different SLAM algorithms, but we are currently using a visual based system using the sub's right and left cameras. This allows us to link the system to Object Detection. | In robot navigation, a SLAM algorithm is used to construct a map of the robot's environment, while simultaneously locating the robot within that map. There are many different SLAM algorithms, but we are currently using a visual based system using the sub's right and left cameras. This allows us to link the system to Object Detection. | ||
| - | The specific system we are using is [[https://github.com/raulmur/ORB_SLAM2|ORB-SLAM2]], an open source feature based visual slam system which we modified for the sub. | + | The specific system we are using is [[https://github.com/raulmur/ORB_SLAM2|ORB-SLAM2]], an open source feature based visual slam system which we modified for the sub. The paper describing the system can be found [[https://arxiv.org/pdf/1610.06475.pdf|here]]. |
| The algorithm works by detecting features (such as edges and corners) in an image, and locates them in space using triangulation with other known map points. | The algorithm works by detecting features (such as edges and corners) in an image, and locates them in space using triangulation with other known map points. | ||
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| ==== Tracking ==== | ==== Tracking ==== | ||
| - | -Tracking localizes the camera by comparing features in a local map. | + | -Tracking localizes the camera by comparing features in a local map. |
| - | -Detects features using the FAST algorithm. | + | -Detects features using the [[https://docs.opencv.org/3.0-beta/doc/py_tutorials/py_feature2d/py_fast/py_fast.html|FAST Algorithm]]. |
| - | -Describes features using ORB algorithm. | + | -Describes features using [[https://opencv-python-tutroals.readthedocs.io/en/latest/py_tutorials/py_feature2d/py_orb/py_orb.html|ORB Algorithm]]. |
| -Selects a new keyframe. | -Selects a new keyframe. | ||
| -If localization is lost, uses Place Recognition module to relocate. | -If localization is lost, uses Place Recognition module to relocate. | ||
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| ==== Local Mapping ==== | ==== Local Mapping ==== | ||
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| + | -Keyframes are added to co-visibility graph Spanning Tree. | ||
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| + | -New Map points are creates by triangulating matching ORB features from different keyframes. | ||
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| + | -Validity of map point is checked by seeing if it is found in other keyframes where it is predicted to be. Must be seen by at least 3 other keyframes. | ||
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| ==== Loop Closing ==== | ==== Loop Closing ==== | ||
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| + | -Loop closing is when the sub recognizes that it has returned to a previous location and adjust map points to accommodate. | ||
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| + | -To detect possible loops, check bag of words vectors in Place Recognition module of the current keyframe and its neighbors in the co-visibility graph. | ||
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| + | -If loop candidate is found preform similarity transform. | ||
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| + | -Fuse map points and preform bundle adjustment. | ||
| ==== Map ==== | ==== Map ==== | ||
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| + | Each map point stores: | ||
| + | *Its 3D position in the world coordinate system. | ||
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| + | *ORB descriptor. | ||
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| + | *The maximum dmax and minimum dmin distances at which the point can be observed, according to the scale invariance limits of the ORB features. | ||
| ==== Place Recognition ==== | ==== Place Recognition ==== | ||
