NeRF-LOAM: Neural Implicit Representation for Large-Scale Incremental LiDAR Odometry and Mapping

This repository contains the implementation of our paper:

NeRF-LOAM: Neural Implicit Representation for Large-Scale Incremental LiDAR Odometry and Mapping (PDF)
Junyuan Deng, Xieyuanli Chen, Songpengcheng Xia, Zhen Sun, Guoqing Liu, Wenxian Yu and Ling Pei
If you use our code in your work, please star our repo and cite our paper.

@misc{deng2023nerfloam,
      title={NeRF-LOAM: Neural Implicit Representation for Large-Scale Incremental LiDAR Odometry and Mapping}, 
      author={Junyuan Deng and Xieyuanli Chen and Songpengcheng Xia and Zhen Sun and Guoqing Liu and Wenxian Yu and Ling Pei},
      year={2023},
      eprint={2303.10709},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

Our incrementally simultaneous odometry and mapping results on the Newer College dataset and the KITTI dataset sequence 00.
The maps are dense with a form of mesh, the red line indicates the odometry results.
We use the same network without training to prove the ability of generalization of our design.

Overview

Overview of our method. Our method is based on our neural SDF and composed of three main components:

Neural odometry takes the pre-processed scan and optimizes the pose via back projecting the queried neural SDF;
Neural mapping jointly optimizes the voxel embeddings map and pose while selecting the key-scans;
Key-scans refined map returns SDF value and the final mesh is reconstructed by marching cube.

Quatitative results

The reconstructed maps The qualitative result of our odometry mapping on the KITTI dataset. From left upper to right bottom, we list the results of sequences 00, 01, 03, 04, 05, 09, 10.

The odometry results The qualitative results of our odometry on the KITTI dataset. From left to right, we list the results of sequences 00, 01, 03, 04, 05, 07, 09, 10. The dashed line corresponds to the ground truth and the blue line to our odometry method.

Data

Newer College real-world LiDAR dataset: website.
MaiCity synthetic LiDAR dataset: website.
KITTI dataset: website.

Code

The code and usage details will be available soon.

Contact

Any questions or suggestions are welcome!

Junyuan Deng: d.juney@sjtu.edu.cn and Xieyuanli Chen: xieyuanli.chen@nudt.edu.cn

License

This project is free software made available under the MIT License. For details see the LICENSE file.

PyNancy / NeRF-LOAM