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| <!> '''Disclaimer:''' RT-SLAM is currently in development and has no official release. It is provided as-is, there is no standalone installation, and it is generally not advised (yet) to people who do not want to get their hands dirty. |
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| RT-SLAM is a fast Slam library and test framework based on EKF. Its main qualities are: * '''genericity''': for sensor models, landmark types, landmark models, landmarks reparametrization, biases estimation; * '''speed''': real time at 60 fps, VGA, gray level; * '''flexibility''': different estimation/image processing sequencing strategies (active search), independent base brick for a hierarchical multimap and multirobots architecture; * '''robustness''': near-optimal repartition of landmarks, data association errors detection (gating, ransac); * '''developer-friendly''': visualization tools (2D and 3D), offline replay step by step, logs, simulation. |
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| For now it provides: * '''Landmarks''': Anchored Homogeneous Points (Inverse Depth) that can be reparametrized into Euclidean Points; * '''Sensors''': Pinhole cameras; * '''Prediction''': Constant velocity model; * '''Data association''': Active search, 1-point Ransac, and mixed strategies. |
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| * Installation procedure * Developer reference |
* [[rtslam/Installation|Installation guide]] * [[rtslam/Using|User guide]] * [[http://homepages.laas.fr/croussil/doc/jafar/group__rtslam.html|Developer guide]] * Contact: jafar@laas.fr, cyril.roussillon@laas.fr |
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| * [[http://homepages.laas.fr/croussil/videos/2010-11-29_rtslam_grande-salle_q25.avi|Demo 1]] (v.a, 1'33, 23Mo, H264 AVI): hand held alone camera at 60 fps indoor, with several loop closures. * [[http://homepages.laas.fr/croussil/videos/2011-02-15_rtslam-inertial-high-dyn.avi|Demo 2]] (v.b, 1'06, 20Mo, H264 AVI): hand held IMU+camera at 50 fps indoor, with very high dynamic. * [[http://homepages.laas.fr/croussil/videos/2011-06-09_rtslam-inertial-robot-grass.avi|Demo 3]] (v.a, 1'25, 27Mo, H264 AVI): IMU+camera on a rover robot on grass, with short term memory (lost landmarks are removed, no loop closure) * [[/Material|Additional videos referenced in papers]] |
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* Stabilize and make a release * Inertial Slam * Odometry Slam * Multimap Slam * Segments Slam == Authors == * [[http://homepages.laas.fr/jsola|Joan Solà]] * [[http://homepages.laas.fr/croussil|Cyril Roussillon]] * [[http://homepages.laas.fr/agonzale|Aurélien Gonzalez]] * [[http://homepages.laas.fr/nmansard|Nicolas Mansard]] * [[http://homepages.laas.fr/jmcodol|Jean-Marie Codol]] |
RT-SLAM
RT-SLAM stands for Real Time SLAM (Simultaneous Localization And Mapping).
Disclaimer: RT-SLAM is currently in development and has no official release. It is provided as-is, there is no standalone installation, and it is generally not advised (yet) to people who do not want to get their hands dirty.
Presentation
RT-SLAM is a fast Slam library and test framework based on EKF. Its main qualities are:
genericity: for sensor models, landmark types, landmark models, landmarks reparametrization, biases estimation;
speed: real time at 60 fps, VGA, gray level;
flexibility: different estimation/image processing sequencing strategies (active search), independent base brick for a hierarchical multimap and multirobots architecture;
robustness: near-optimal repartition of landmarks, data association errors detection (gating, ransac);
developer-friendly: visualization tools (2D and 3D), offline replay step by step, logs, simulation.
For now it provides:
Landmarks: Anchored Homogeneous Points (Inverse Depth) that can be reparametrized into Euclidean Points;
Sensors: Pinhole cameras;
Prediction: Constant velocity model;
Data association: Active search, 1-point Ransac, and mixed strategies.
Documentation
Screenshots and videos
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Demo 1 (v.a, 1'33, 23Mo, H264 AVI): hand held alone camera at 60 fps indoor, with several loop closures.
Demo 2 (v.b, 1'06, 20Mo, H264 AVI): hand held IMU+camera at 50 fps indoor, with very high dynamic.
Demo 3 (v.a, 1'25, 27Mo, H264 AVI): IMU+camera on a rover robot on grass, with short term memory (lost landmarks are removed, no loop closure)
Roadmap
- Stabilize and make a release
- Inertial Slam
- Odometry Slam
- Multimap Slam
- Segments Slam