# ATDN vSLAM: An all-through Deep Learning-Based Solution for Visual Simultaneous Localization and Mapping

arXiv: https://arxiv.org/abs/2206.05963 · HTML: https://mlsanity.com/paper/2206.05963/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 20 of its 45 references that are in the map.
- First posted on arXiv 13 Jun 2022.

## Builds on (most cited first, 20 of 20)

- [PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation](https://mlsanity.com/paper/1612.00593/) (2016)
- [Scalability in Perception for Autonomous Driving: Waymo Open Dataset](https://mlsanity.com/paper/1912.04838/) (2019)
- [ORB-SLAM: a Versatile and Accurate Monocular SLAM System](https://mlsanity.com/paper/1502.00956/) (2015)
- [ORB-SLAM2: an Open-Source SLAM System for Monocular, Stereo and RGB-D Cameras](https://mlsanity.com/paper/1610.06475/) (2016)
- [Unsupervised Learning of Depth and Ego-Motion from Video](https://mlsanity.com/paper/1704.07813/) (2017)
- [KITTI-360: A Novel Dataset and Benchmarks for Urban Scene Understanding in 2D and 3D](https://mlsanity.com/paper/2109.13410/) (2021)
- [Direct Sparse Odometry](https://mlsanity.com/paper/1607.02565/) (2016)
- [Past, Present, and Future of Simultaneous Localization And Mapping: Towards the Robust-Perception Age](https://mlsanity.com/paper/1606.05830/) (2016)
- [Learning to Estimate Hidden Motions with Global Motion Aggregation](https://mlsanity.com/paper/2104.02409/) (2021)
- [D3VO: Deep Depth, Deep Pose and Deep Uncertainty for Monocular Visual Odometry](https://mlsanity.com/paper/2003.01060/) (2020)
- [DeepVO: Towards End-to-End Visual Odometry with Deep Recurrent Convolutional Neural Networks](https://mlsanity.com/paper/1709.08429/) (2017)
- [CNN-SLAM: Real-time dense monocular SLAM with learned depth prediction](https://mlsanity.com/paper/1704.03489/) (2017)
- [CodeSLAM - Learning a Compact, Optimisable Representation for Dense Visual SLAM](https://mlsanity.com/paper/1804.00874/) (2018)
- [SemanticFusion: Dense 3D Semantic Mapping with Convolutional Neural Networks](https://mlsanity.com/paper/1609.05130/) (2016)
- [LDSO: Direct Sparse Odometry with Loop Closure](https://mlsanity.com/paper/1808.01111/) (2018)
- [A Survey on Deep Learning for Localization and Mapping: Towards the Age of Spatial Machine Intelligence](https://mlsanity.com/paper/2006.12567/) (2020)
- [Visual-Inertial Monocular SLAM with Map Reuse](https://mlsanity.com/paper/1610.05949/) (2016)
- [Inertial-Only Optimization for Visual-Inertial Initialization](https://mlsanity.com/paper/2003.05766/) (2020)
- [Learning Monocular Visual Odometry through Geometry-Aware Curriculum Learning](https://mlsanity.com/paper/1903.10543/) (2019)
- [A Hybrid Sparse-Dense Monocular SLAM System for Autonomous Driving](https://mlsanity.com/paper/2108.07736/) (2021)

## Abstract (arXiv)

In this paper, a novel solution is introduced for visual Simultaneous Localization and Mapping (vSLAM) that is built up of Deep Learning components. The proposed architecture is a highly modular framework in which each component offers state of the art results in their respective fields of vision-based deep learning solutions. The paper shows that with the synergic integration of these individual building blocks, a functioning and efficient all-through deep neural (ATDN) vSLAM system can be created. The Embedding Distance Loss function is introduced and using it the ATDN architecture is trained. The resulting system managed to achieve 4.4% translation and 0.0176 deg/m rotational error on a subset of the KITTI dataset. The proposed architecture can be used for efficient and low-latency autonomous driving (AD) aiding database creation as well as a basis for autonomous vehicle (AV) control.