# Online Mutual Adaptation of Deep Depth Prediction and Visual SLAM

arXiv: https://arxiv.org/abs/2111.04096 · HTML: https://mlsanity.com/paper/2111.04096/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 30 of its 55 references that are in the map.
- First posted on arXiv 7 Nov 2021.

## Builds on (most cited first, 30 of 30)

- [Overcoming catastrophic forgetting in neural networks](https://mlsanity.com/paper/1612.00796/) (2016)
- [ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenes](https://mlsanity.com/paper/1702.04405/) (2017)
- [What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?](https://mlsanity.com/paper/1703.04977/) (2017)
- [Vision Transformers for Dense Prediction](https://mlsanity.com/paper/2103.13413/) (2021)
- [Towards Robust Monocular Depth Estimation: Mixing Datasets for Zero-shot Cross-dataset Transfer](https://mlsanity.com/paper/1907.01341/) (2019)
- [Continual Lifelong Learning with Neural Networks: A Review](https://mlsanity.com/paper/1802.07569/) (2018)
- [Memory Aware Synapses: Learning what (not) to forget](https://mlsanity.com/paper/1711.09601/) (2017)
- [ORB-SLAM3: An Accurate Open-Source Library for Visual, Visual-Inertial and Multi-Map SLAM](https://mlsanity.com/paper/2007.11898/) (2020)
- [Unsupervised Monocular Depth Estimation with Left-Right Consistency](https://mlsanity.com/paper/1609.03677/) (2016)
- [Unsupervised Learning of Depth and Ego-Motion from Video](https://mlsanity.com/paper/1704.07813/) (2017)
- [Deep Ordinal Regression Network for Monocular Depth Estimation](https://mlsanity.com/paper/1806.02446/) (2018)
- [AdaBins: Depth Estimation using Adaptive Bins](https://mlsanity.com/paper/2011.14141/) (2020)
- [From Coarse to Fine: Robust Hierarchical Localization at Large Scale](https://mlsanity.com/paper/1812.03506/) (2018)
- [Direct Sparse Odometry](https://mlsanity.com/paper/1607.02565/) (2016)
- [3D Packing for Self-Supervised Monocular Depth Estimation](https://mlsanity.com/paper/1905.02693/) (2019)
- [Past, Present, and Future of Simultaneous Localization And Mapping: Towards the Robust-Perception Age](https://mlsanity.com/paper/1606.05830/) (2016)
- [Unsupervised CNN for Single View Depth Estimation: Geometry to the Rescue](https://mlsanity.com/paper/1603.04992/) (2016)
- [Limitations of the Empirical Fisher Approximation for Natural Gradient Descent](https://mlsanity.com/paper/1905.12558/) (2019)
- [D3VO: Deep Depth, Deep Pose and Deep Uncertainty for Monocular Visual Odometry](https://mlsanity.com/paper/2003.01060/) (2020)
- [DeMoN: Depth and Motion Network for Learning Monocular Stereo](https://mlsanity.com/paper/1612.02401/) (2016)
- [BA-Net: Dense Bundle Adjustment Network](https://mlsanity.com/paper/1806.04807/) (2018)
- [Continuous Learning in Single-Incremental-Task Scenarios](https://mlsanity.com/paper/1806.08568/) (2018)
- [Deep Virtual Stereo Odometry: Leveraging Deep Depth Prediction for Monocular Direct Sparse Odometry](https://mlsanity.com/paper/1807.02570/) (2018)
- [DiverseDepth: Affine-invariant Depth Prediction Using Diverse Data](https://mlsanity.com/paper/2002.00569/) (2020)
- [Pseudo RGB-D for Self-Improving Monocular SLAM and Depth Prediction](https://mlsanity.com/paper/2004.10681/) (2020)
- [Photometric Bundle Adjustment for Vision-Based SLAM](https://mlsanity.com/paper/1608.02026/) (2016)
- [Semi-Supervised Monocular Depth Estimation with Left-Right Consistency Using Deep Neural Network](https://mlsanity.com/paper/1905.07542/) (2019)
- [CNN-SVO: Improving the Mapping in Semi-Direct Visual Odometry Using Single-Image Depth Prediction](https://mlsanity.com/paper/1810.01011/) (2018)
- [VITAMIN-E: VIsual Tracking And MappINg with Extremely Dense Feature Points](https://mlsanity.com/paper/1904.10324/) (2019)
- [DeepRelativeFusion: Dense Monocular SLAM using Single-Image Relative Depth Prediction](https://mlsanity.com/paper/2006.04047/) (2020)

## Built on by (most cited first, 1 of 1)

- [HDPV-SLAM: Hybrid Depth-augmented Panoramic Visual SLAM for Mobile Mapping System with Tilted LiDAR and Panoramic Visual Camera](https://mlsanity.com/paper/2301.11823/) (2023)

## Abstract (arXiv)

The ability of accurate depth prediction by a convolutional neural network (CNN) is a major challenge for its wide use in practical visual simultaneous localization and mapping (SLAM) applications, such as enhanced camera tracking and dense mapping. This paper is set out to answer the following question: Can we tune a depth prediction CNN with the help of a visual SLAM algorithm even if the CNN is not trained for the current operating environment in order to benefit the SLAM performance? To this end, we propose a novel online adaptation framework consisting of two complementary processes: a SLAM algorithm that is used to generate keyframes to fine-tune the depth prediction and another algorithm that uses the online adapted depth to improve map quality. Once the potential noisy map points are removed, we perform global photometric bundle adjustment (BA) to improve the overall SLAM performance. Experimental results on both benchmark datasets and a real robot in our own experimental environments show that our proposed method improves the overall SLAM accuracy. While regularization has been shown to be effective in multi-task classification problems, we present experimental results and an ablation study to show the effectiveness of regularization in preventing catastrophic forgetting in the online adaptation of depth prediction, a single-task regression problem. In addition, we compare our online adaptation framework against the state-of-the-art pre-trained depth prediction CNNs to show that our online adapted depth prediction CNN outperforms the depth prediction CNNs that have been trained on a large collection of datasets.