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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, there is some code to adapt for your own use, and it is generally not advised (yet) to people who do not want to get their hands dirty. |
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| * '''speed''': real time at 60 fps, VGA, gray level; | * '''speed''': real time at 60 fps (VGA, gray level) on a decent machine (at least one Core2 core at 2.2GHz) |
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| * '''open source''': in C++ for Linux and MacOS | |
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| * '''Prediction''': Constant velocity model; | * '''Prediction''': Constant velocity model, inertial sensor; |
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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): hand held alone camera at 60 fps indoor, with several loop closures. | * [[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]] |
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| * Inertial Slam |
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
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
Roadmap
- Stabilize and make a release
- Odometry Slam
- Multimap Slam
- Segments Slam