MultiSlam_DiffPose

Multi-Session SLAM with Differentiable Wide-Baseline Pose Optimization

This repository contains the source code for our paper:

Multi-Session SLAM with Differentiable Wide-Baseline Pose Optimization

Lahav Lipson, Jia Deng

@inproceedings{lipson2024multi,
  title={Multi-Session SLAM with Differentiable Wide-Baseline Pose Optimization},
  author={Lipson, Lahav and Deng, Jia},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year={2024}
}

Installation

git clone --recursive [email protected]:princeton-vl/MultiSlam_DiffPose.git
cd MultiSlam_DiffPose
conda env create --file environment.yml --name msdp
conda activate msdp

You will also need to install the third party libraries hloc

cd thirdparty/Hierarchical-Localization
python -m pip install -e .
cd ../..

and eigen

wget https://gitlab.com/libeigen/eigen/-/archive/3.4.0/eigen-3.4.0.zip
unzip eigen-3.4.0.zip -d thirdparty

Finally, run

pip install .

Download model weights

We provide the model weights for the VO backbone, the two-view backbone, and the two-view backbone after homography pre-training:

https://drive.google.com/drive/folders/11iC4ZAmO_mWMUjkpS83HgVcS80hFL-30?usp=sharing

Two-view Demo

Run conda install jupyter if not done previously.

We provide notebooks to demo our two-view pose method. The function run_model(model, images, intrinsics) outputs a list of intermediate predictions of pose/matches. The last (best) prediction is of the form

$$predictions[-1] = (pts1 in {Bbb R}^{N times 2}, pts2 in {Bbb R}^{N times 2}, confidence in {Bbb R}^{N}, rel_pose in {Bbb R}^{4times 4})$$

To visualize predictions on Scannet / megadepth, follow the two-view data download instructions and run

jupyter notebook demo_scannet_megadepth.ipynb

To visualize a prediction on any image pair, edit and run demo_pair.ipynb

jupyter notebook demo_pair.ipynb

Evaluation/Demo Data Preparation

Two-View

The authors of LoFTR generously provide the testing sets for Scannet and Megadepth. Download and unpack them into data/scannet/scannet_test_1500/ and data/megadepth/megadepth_test_1500/, respectively.

untar megadepth_test_1500.tar -C data/megadepth/
untar scannet_test_1500.tar -C data/scannet/

Multi-Session SLAM

EuRoC: Download the sequences from the EuRoC dataset here. Make sure to download the ASL format. Unpack the sequences under data/EuRoC

ETH3D: You can download the sequences from the ETH3D training dataset using their provided script download_eth3d_slam_datasets.py. You can select mono, RGB only. Unpack the sequences under data/ETH3D

Evaluation

Multi-Session SLAM

To evaluate our full Multi-Session SLAM approach on all EuRoC sequence groups, run

python eval_euroc.py 'Vicon 1'
python eval_euroc.py 'Vicon 2'
python eval_euroc.py 'Machine Hall'
python eval_euroc.py 'Machine Hall0-3'

To evaluate our method on the ETH3D sequence groups, run

python eval_eth3d.py sofa
python eval_eth3d.py table
python eval_eth3d.py plant_scene
python eval_eth3d.py einstein
python eval_eth3d.py planar

Both scripts follow the same template. Extending the pipeline to new data only requires implementing a dataloader for loading images and intrinsics.

Two-view Pose

To evaluate our two-view pose method on Scannet, run

python evaluate.py --dataset test_scannet --load_ckpt twoview.pth -o ScanNetDatasetWrapper.pad_to_size=840

For Megadepth, run

python evaluate.py --dataset test_megadepth --load_ckpt twoview.pth

Training

Data download

Synthetic Homographies: Run the download script in https://github.com/filipradenovic/revisitop to download the Oxford-Paris distractors dataset. Store the files under data/revisitop1m/jpg/

Scannet/Megadepth: Follow the instructions from the LoFTR training data setup: https://github.com/zju3dv/LoFTR/blob/master/docs/TRAINING.md. Unpack the *_indices.tar into index subfolders.

VO data download: To download the data for training the VO backbone, follow the download instructions from the DROID-SLAM repo.

The full data layout should be as follows:

├── data
    ├── revisitop1m
        ├── jpg
        ├── revisitop1m.txt
    ├── scannet
        ├── index
        ├── scannet_test_1500
        ├── train
    ├── megadepth
        ├── index
        ├── scannet_test_1500
        ├── train
    ├── TartanAir
        ├── abandonedfactory
        ├── ...

Homography Two-view Pre-training

On one or several A6000s (we used 1), run

python train.py -g train_homog.gin --batch_size 14 --name homog_pretrain
mv model_weights/homog_pretrain/step_140000.pth homog_pretrain.pth

Two-view full training

On one or several A6000s (we used 10), run

python train.py -g train_pose.gin --batch_size 12 --name twoview --load_ckpt homog_pretrain.pth
mv model_weights/twoview/step_100000.pth twoview.pth 

VO training

On one or several A6000s (we used 1), run

python train_vo.py --steps=240000 --lr=0.00008 --name=vo
mv checkpoints/vo_240000.pth vo.pth

Acknowledgements

This project relies on code from existing repositories:

• Hierarchical-Localization
• LoFTR
• DPVO
• Pytorch3D

Thank you to the authors for open-sourcing their code