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RTAB-Map RTAB-Map : Real-Time Appearance-Based Mapping


Loop closure detection is the process involved when trying to find a match between the current and a previously visited locations in SLAM (Simultaneous Localization And Mapping). Over time, the amount of time required to process new observations increases with the size of the internal map, which may affect real-time processing. RTAB-Map is a novel real-time loop closure detection approach for large-scale and long-term SLAM. Our approach is based on efficient memory management to keep computation time for each new observation under a fixed time limit, thus respecting real-time limit for long-term operation. Results demonstrate the approach's adaptability and scalability using two custom data sets and ten standard data sets.

Example of sensorimotor learning using directly this loop closure detection approach (new in SeMoLearning) :


Note that these results (more recent) may differ from those in the presentation video above...

Figure 1: Summary of the loop closures detected on UdeS data set :

  • Green : Loop closures detected
  • Yellow : Loop closures rejected
  • Red : Unable to detect a loop closure because old places could not be retrieved

Figure 2: Processing time for each image acquired (real-time limit fixed to 700 ms for an image rate of 1 Hz)

Figure 3: Precision-Recall (48% recall at 100% precision)

RTAB-Map LoopClosureMapResults.png RTAB-Map LoopClosureTimeResults.png RTAB-Map RecallResults.png


  • Newer:
  • Older:

Source code[edit]

The code was tested on Windows (Xp, 7), Mac OS X 10.6 and Ubuntu 10.4LTS.

Images acquired in Need For Speed Most Wanted

Data sets[edit]


  • 5395 images at 1 Hz (1.5 hours).
  • Images taken while walking through a loop of ~2 km, traversed two times.
  • The data set contains indoor and outdoor environments.
UdeS_1Hz GroundTruth


  • 25098 images at 1 Hz (7 hours).
  • Images taken from the racing video game Need For Speed: Most Wanted.
  • 2 areas visited hundred times each (100 traversals in area 1 then moved to area 2 for another 102 traversals).


Community data sets from other loop closure detection approaches :

Ground truths:

  • NewCollege.rar 1073 images at ~0.5 Hz (left and right images merged) (updated October 13 2014)
  • CityCentre.rar 1237 images at ~0.5 Hz (left and right images merged) (updated October 13 2014)
  • Lip6Indoor.rar 388 images at 1 Hz
  • Lip6Outdoor.rar 531 images at 0.5 Hz
  • Eynsham70km.rar 5519 images at ~1 Hz (Note that we removed some images of the original data set to have an approximately image rate of 1 Hz)


  1. M. Labbé and F. Michaud, “Online Global Loop Closure Detection for Large-Scale Multi-Session Graph-Based SLAM,” in Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, 2014. (pdf)
  2. Labbé, M., Michaud., F. (2013), “Appearance-based loop closure detection in real-time for large-scale and long-term operation,” IEEE Transactions on Robotics, vol. 29, no. 3, pp. 734-745. (pdf) (IEEE Xplore)
  3. Labbé, M., Michaud, F. (2011), “Memory management for real-time appearance-based loop closure detection,” in Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems. (pdf) (IEEE Xplore)