Deep Learning for Simultaneous Localization and Mapping (SLAM)

Simultaneous Localization and Mapping (SLAM) is a fundamental problem in mobile robotics that allows a robot to localize itself against a previously unseen environment while simultaneously constructing a representation of it. With the recent resurgence in deep learning techniques, challenges in the traditional-geometry-based SLAM have been addressed with learning-based techniques. Similarly, the multiview localization capability of SLAM has been exploited to learn better models. While progress is being made in traditional-geometry-based SLAM techniques, deep learning introduces a new set of tools that can be leveraged to further improve the performance of SLAM systems.

This Special Issue focuses on Simultaneous Localization and Mapping in general and encourages submissions that further the state of the art of both geometry-based SLAM and methods that focus on how deep learning can help SLAM. Topics of interest for the Special Issue include, but are not limited to:

• localization;
• mapping;
• place recognition under appearance change;
• loop closure detection;
• topological and metric relocalization;
• Deep Learning for localization;
• learned methods for mapping ;
• end-to-end deeply learned Simultaneous Localization and Mapping (SLAM);
• outlier-robust SLAM;
• SLAM with novel sensors; and
• deep learned priors for SLAM.

Dr. Yasir Latif
Dr. Pulak Purkait
Guest Editor

Manuscript Submission Information

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