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

arXiv: https://arxiv.org/abs/2006.12567 · HTML: https://mlsanity.com/paper/2006.12567/

## Key facts

- Cited by 35 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Jun 2020.

## Built on by (most cited first, 30 of 35)

- [How NeRFs and 3D Gaussian Splatting are Reshaping SLAM: a Survey](https://mlsanity.com/paper/2402.13255/) (2024)
- [Wide-Baseline Relative Camera Pose Estimation with Directional Learning](https://mlsanity.com/paper/2106.03336/) (2021)
- [Convolutional Cross-View Pose Estimation](https://mlsanity.com/paper/2303.05915/) (2023)
- [Differentiable SLAM-net: Learning Particle SLAM for Visual Navigation](https://mlsanity.com/paper/2105.07593/) (2021)
- [Img2Loc: Revisiting Image Geolocalization using Multi-modality Foundation Models and Image-based Retrieval-Augmented Generation](https://mlsanity.com/paper/2403.19584/) (2024)
- [The Surprising Effectiveness of Visual Odometry Techniques for Embodied PointGoal Navigation](https://mlsanity.com/paper/2108.11550/) (2021)
- [Deep Online Correction for Monocular Visual Odometry](https://mlsanity.com/paper/2103.10029/) (2021)
- [A Critical Analysis of Image-based Camera Pose Estimation Techniques](https://mlsanity.com/paper/2201.05816/) (2022)
- [Semantic Visual Simultaneous Localization and Mapping: A Survey](https://mlsanity.com/paper/2209.06428/) (2022)
- [MDN-VO: Estimating Visual Odometry with Confidence](https://mlsanity.com/paper/2112.12812/) (2021)
- [Recent Advancements in Deep Learning Applications and Methods for Autonomous Navigation: A Comprehensive Review](https://mlsanity.com/paper/2302.11089/) (2023)
- [Information-Theoretic Odometry Learning](https://mlsanity.com/paper/2203.05724/) (2022)
- [Comparison of modern open-source visual SLAM approaches](https://mlsanity.com/paper/2108.01654/) (2021)
- [A review of laser scanning for geological and geotechnical applications in underground mining](https://mlsanity.com/paper/2211.11181/) (2022)
- [TransFusionOdom: Interpretable Transformer-based LiDAR-Inertial Fusion Odometry Estimation](https://mlsanity.com/paper/2304.07728/) (2023)
- [Energy-Based Models for Cross-Modal Localization using Convolutional Transformers](https://mlsanity.com/paper/2306.04021/) (2023)
- [Multi-Camera Sensor Fusion for Visual Odometry using Deep Uncertainty Estimation](https://mlsanity.com/paper/2112.12818/) (2021)
- [Multi-modal Loop Closure Detection with Foundation Models in Severely Unstructured Environments](https://mlsanity.com/paper/2511.05404/) (2025)
- [Attention-based Vehicle Self-Localization with HD Feature Maps](https://mlsanity.com/paper/2107.07787/) (2021)
- [Sparse Image based Navigation Architecture to Mitigate the need of precise Localization in Mobile Robots](https://mlsanity.com/paper/2203.15272/) (2022)
- [IDLS: Inverse Depth Line based Visual-Inertial SLAM](https://mlsanity.com/paper/2304.11748/) (2023)
- [A survey on deep learning approaches for data integration in autonomous driving system](https://mlsanity.com/paper/2306.11740/) (2023)
- [From Variance to Veracity: Unbundling and Mitigating Gradient Variance in Differentiable Bundle Adjustment Layers](https://mlsanity.com/paper/2406.07785/) (2024)
- [A Neurosymbolic Approach to Adaptive Feature Extraction in SLAM](https://mlsanity.com/paper/2407.06889/) (2024)
- [Near Field Localization via AI-Aided Subspace Methods](https://mlsanity.com/paper/2504.00599/) (2025)
- [ATDN vSLAM: An all-through Deep Learning-Based Solution for Visual Simultaneous Localization and Mapping](https://mlsanity.com/paper/2206.05963/) (2022)
- [Localizing Anatomical Landmarks in Ocular Images using Zoom-In Attentive Networks](https://mlsanity.com/paper/2210.02445/) (2022)
- [An Improved Multi-State Constraint Kalman Filter for Visual-Inertial Odometry](https://mlsanity.com/paper/2210.08117/) (2022)
- [Split-KalmanNet: A Robust Model-Based Deep Learning Approach for SLAM](https://mlsanity.com/paper/2210.09636/) (2022)
- [On the Application of Efficient Neural Mapping to Real-Time Indoor Localisation for Unmanned Ground Vehicles](https://mlsanity.com/paper/2211.04718/) (2022)

## Abstract (arXiv)

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.