https://github.com/ai4ce/DeepMapping2

[CVPR2023] DeepMapping2: Self-Supervised Large-Scale LiDAR Map Optimization
https://github.com/ai4ce/DeepMapping2

deepmapping large-scale-mapping lidar lidar-mapping lidar-slam mapping slam training-as-optimization

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[CVPR2023] DeepMapping2: Self-Supervised Large-Scale LiDAR Map Optimization

• Host: GitHub
• URL: https://github.com/ai4ce/DeepMapping2
• Owner: ai4ce
• License: mit
• Created: 2022-03-21T03:07:36.000Z (over 2 years ago)
• Default Branch: main
• Last Pushed: 2024-06-26T08:53:44.000Z (5 months ago)
• Last Synced: 2024-08-01T03:41:13.075Z (3 months ago)
• Topics: deepmapping, large-scale-mapping, lidar, lidar-mapping, lidar-slam, mapping, slam, training-as-optimization
• Language: Python
• Homepage: https://ai4ce.github.io/DeepMapping2/
• Size: 31.2 MB
• Stars: 153
• Watchers: 3
• Forks: 16
• Open Issues: 1
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# DeepMapping2: Self-Supervised Large-Scale LiDAR Map Optimization

[[PDF]](https://arxiv.org/abs/2212.06331)[[Website]](https://ai4ce.github.io/DeepMapping2/)

[Chao Chen](https://joechencc.github.io/)\*, [Xinhao Liu](https://gaaaavin.github.io/)\*, [Yiming Li](https://roboticsyimingli.github.io/), [Li Ding](https://www.hajim.rochester.edu/ece/lding6/), [Chen Feng](https://scholar.google.com/citations?user=YeG8ZM0AAAAJ)









## News

**[2023-06]** 🔥 Quaternion has been added as the default rotation representation, resulting in faster convergence from 30 epochs to 7 epochs.

**[2023-06]** 🔥 Detailed instruction is available for implementing DeepMapping2 on a custom dataset.

**[2023-03]** The camera-ready version of our paper is available at [arXiv](https://arxiv.org/abs/2212.06331). **New figures** are added to the supplementary material.

**[2023-02]** Our paper is accepted by [CVPR 2023](https://cvpr2023.thecvf.com/).

## Abstract

LiDAR mapping is important yet challenging in self-driving and mobile robotics. To tackle such a global point cloud registration problem, DeepMapping converts the complex map estimation into a self-supervised training of simple deep networks. Despite its broad convergence range on small datasets, DeepMapping still cannot produce satisfactory results on large-scale datasets with thousands of frames. This is due to the lack of loop closures and exact cross-frame point correspondences, and the slow convergence of its global localization network. We propose DeepMapping2 by adding two novel techniques to address these issues: (1) organization of training batch based on map topology from loop closing, and (2) self-supervised local-to-global point consistency loss leveraging pairwise registration. Our experiments and ablation studies on public datasets (KITTI, NCLT, and Nebula) demonstrate the effectiveness of our method. Our code will be released.

# Getting Started:

## Installation

The code is tested with Python 3.9, PyTorch 1.13.1, and CUDA 11.6.

To install the dependencies, you can create a virtual environment with

```

conda env create -f environment.yml

```

## Data Preparation

To download the dataset used for training and testing, please refer to [./data/README.md](./data/README.md)

## Usage

To train the model, execute the script

```

cd script/

./run_train.sh

```

The visualization and evaluation results will be saved in the `results` folder.

To use a different initial pose and pairwise registration, please edit `INIT` and `PAIRWISE` in the script to direct to the corresponding files.

## Citation

If you find this work useful for your research, please cite our paper:

```

@inproceedings{chen2023deepmapping2,

  title={DeepMapping2: Self-Supervised Large-Scale LiDAR Map Optimization},

  author={Chen, Chao and Liu, Xinhao and Li, Yiming and Ding, Li and Feng, Chen},

  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},

  pages={9306--9316},

  year={2023}

}

```

## Related Project

[DeepMapping (CVPR'2019 oral)](https://github.com/ai4ce/DeepMapping)