Difference between revisions of "RTAB-Map"
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== Publications ==
== Publications ==
Labbé, M., Michaud, F. (2011), “Memory management approach for real-time appearance-based loop closure detection”,
Labbé, M., Michaud, F. (2011), “Memory management approach for real-time appearance-based loop closure detection”, IEEE Transactions on Robotics.
Revision as of 13:09, 15 April 2011
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 one custom data set and four standard data sets.
Note that these results (more recent) may differ from those in the video...
Figure 1: Summary of the loop closures detected on UdeS1Hz 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 (43% recall at 100% precision)
The code was tested on Windows (Xp, 7), Mac OS X 10.6 and Ubuntu 10.4LTS.
Community data sets from other loop closure detection approaches :
Labbé, M., Michaud, F. (2011), “Memory management approach for real-time appearance-based loop closure detection”, submitted to IEEE Transactions on Robotics.