RTAB-Map : Real-Time Appearance-Based Mapping A loop closure detection application for robotics applications.
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detection robot memory management

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

RTAB-Map : Real-Time Appearance-Based Mapping or RTAB-Map is a memory management approach for real-time appearance-based loop closure detection for autonomous robot (using a simple camera or webcam). The loop closure detection approach is fully incremental, starting with an empty memory. The robot builds his own representation of the environment by linking new acquired images with previous ones (e.g., detecting loop closures). The method implemented here can be considered as a Topological SLAM (Simultaneous Localization And Mapping) approach. Memory management makes it possible to process each new image under a fixed real-time limit, thus ideal for long-term operation and systems with limited resources. Main features: Loop closure hypotheses are evaluated using a Bayes filter Image matching is done using a "bag-of-words" approach (with SURF features - OpenCV) Memory is divided into short-term, working and long-term memories Sqlite3 database is used for the long-term memory

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