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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) on a decent machine (at least one Core2 core at 2.2GHz) * '''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. * '''open source''': in C++ for Linux and MacOS |
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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, inertial sensor; * '''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]] (Doxygen documentation) * [[http://trac.laas.fr/git/rtslam-jafar|Git web interface]] (source code browsing) * Contact: rtslam-users@laas.fr (please always '''reply all''' to messages to include the mailing list; if you are interested you can subscribe to this alias [[https://sympa.laas.fr/sympa/info/rtslam-users|there]]) |
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| ||<tablestyle="width:100%;text-align:center;" style="border:0px"> {{attachment:rtslam3D.png|3D view|width=400px}}||<style="border:0px"> {{attachment:rtslam2D.png|2D view|width=400px}} || * [[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]] == Papers == * __RT-SLAM: A Generic and Real-Time Visual SLAM Implementation__. Cyril Roussillon, Aurélien Gonzalez, Joan Solà, Jean-Marie Codol, Nicolas Mansard, Simon Lacroix, and Michel Devy. ICVS 2011, volume 6962 of Lecture Notes in Computer Science, page 31-40. Springer. [[http://homepages.laas.fr/croussil/publications/2011_ICVS_rtslam.pdf|Download PDF]] * __High rate-localization for high-speed all-terrain robots__. Cyril Roussillon, Simon Lacroix. CCCA 2012. [[http://homepages.laas.fr/croussil/publications/2012_CCCA_rtslam.pdf|Download PDF]] |
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* Stabilize and make a release * Odometry Slam * Multimap Slam * Segments Slam == Authors == * Contact for general questions: rtslam-users@laas.fr * [[http://joansola.eu|Joan Solà]] * [[http://pro.cyril-roussillon.fr|Cyril Roussillon]] * [[http://homepages.laas.fr/agonzale|Aurélien Gonzalez]] * [[http://homepages.laas.fr/nmansard|Nicolas Mansard]] * [[http://homepages.laas.fr/jmcodol|Jean-Marie Codol]] * [[http://homepages.laas.fr/dmarquez|David Marquez]] |
RT-SLAM
RT-SLAM stands for Real Time SLAM (Simultaneous Localization And Mapping).
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) on a decent machine (at least one Core2 core at 2.2GHz)
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.
open source: in C++ for Linux and MacOS
For now it provides:
Landmarks: Anchored Homogeneous Points (Inverse Depth) that can be reparametrized into Euclidean Points;
Sensors: Pinhole cameras;
Prediction: Constant velocity model, inertial sensor;
Data association: Active search, 1-point Ransac, and mixed strategies.
Documentation
Developer guide (Doxygen documentation)
Git web interface (source code browsing)
Contact: rtslam-users@laas.fr (please always reply all to messages to include the mailing list; if you are interested you can subscribe to this alias there)
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)
Papers
RT-SLAM: A Generic and Real-Time Visual SLAM Implementation. Cyril Roussillon, Aurélien Gonzalez, Joan Solà, Jean-Marie Codol, Nicolas Mansard, Simon Lacroix, and Michel Devy. ICVS 2011, volume 6962 of Lecture Notes in Computer Science, page 31-40. Springer. Download PDF
High rate-localization for high-speed all-terrain robots. Cyril Roussillon, Simon Lacroix. CCCA 2012. Download PDF
Roadmap
- Stabilize and make a release
- Odometry Slam
- Multimap Slam
- Segments Slam
Authors
Contact for general questions: rtslam-users@laas.fr