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.

Installation guide
User guide
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)

3D view

2D view

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)
Additional videos referenced in 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