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Deep learning based localization and mapping has recently attractedsignificant attention. Instead of creating hand-designed algorithms throughexploitation of physical models or geometric theories, deep learning basedsolutions provide an alternative to solve the problem in a data-driven way.Benefiting from ever-increasing volumes of data and computational power, thesemethods are fast evolving into a new area that offers accurate and robustsystems to track motion and estimate scenes and their structure for real-worldapplications. In this work, we provide a comprehensive survey, and propose anew taxonomy for localization and mapping using deep learning. We also discussthe limitations of current models, and indicate possible future directions. Awide range of topics are covered, from learning odometry estimation, mapping,to global localization and simultaneous localization and mapping (SLAM). Werevisit the problem of perceiving self-motion and scene understanding withon-board sensors, and show how to solve it by integrating these modules into aprospective spatial machine intelligence system (SMIS). It is our hope thatthis work can connect emerging works from robotics, computer vision and machinelearning communities, and serve as a guide for future researchers to apply deeplearning to tackle localization and mapping problems.

A Survey on Deep Learning for Localization and Mapping: Towards the Age of Spatial Machine Intelligence