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

TL;DR

This survey reviews recent advances in deep learning for localization and mapping, highlighting new taxonomy, current limitations, and future directions for integrating these methods into spatial machine intelligence systems.

Contribution

It provides a comprehensive taxonomy and analysis of deep learning approaches for localization and mapping, connecting robotics, computer vision, and machine learning fields.

Findings

Deep learning methods improve accuracy and robustness in localization and mapping.

Current models face limitations in generalization and real-world deployment.

Future research directions include integrating modules into spatial machine intelligence systems.

Abstract

Deep learning based localization and mapping has recently attracted significant attention. Instead of creating hand-designed algorithms through exploitation of physical models or geometric theories, deep learning based solutions provide an alternative to solve the problem in a data-driven way. Benefiting from ever-increasing volumes of data and computational power, these methods are fast evolving into a new area that offers accurate and robust systems to track motion and estimate scenes and their structure for real-world applications. In this work, we provide a comprehensive survey, and propose a new taxonomy for localization and mapping using deep learning. We also discuss the limitations of current models, and indicate possible future directions. A wide range of topics are covered, from learning odometry estimation, mapping, to global localization and simultaneous localization and mapping (SLAM). We revisit the problem of perceiving self-motion and scene understanding with on-board sensors, and show how to solve it by integrating these modules into a prospective spatial machine intelligence system (SMIS). It is our hope that this work can connect emerging works from robotics, computer vision and machine learning communities, and serve as a guide for future researchers to apply deep learning to tackle localization and mapping problems.