Neural Inertial Localization

Paper: CVPR 2022, arXiv
Website: https://sachini.github.io/niloc
Demo: https://youtu.be/FmkfUKhKe2Q

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This is the implementation of the approach described in the paper.

Herath, S., Caruso, D., Liu, C., Chen, Y. and Furukawa, Y., Neural Inertial Localization. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2022.

We provide the code for reproducing our results, datasets as well as pre-trained models.

• Dataset : Dropbox
• Models : Dropbox

Please cite the following paper is you use the code, paper, models or data.

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Instructions

1. Setup conda environment from niloc_env.yaml

2. Follow instructions on preprocess/README.md to preprocess real data and optionally, generate synthetic data.

3. Setup necessary file paths in niloc/config. (dataset: dataset paths, grid: map image paths, io: output paths)

4. [Optional] Pretrain using IMU + synthetic data. Parameters used in paper are set as defaults.

   ./train_synthetic.sh <building>
   

5. Train using IMU data. [Optional] load pretrained weights.

   ./train_imu.sh <building> [<path to pretrained checkpoint>]
   

6. Evaluate

   • Select the checkpoints to use and create checkpoint file described in niloc/cmd_test_file.py

   ./test_imu.sh <building> <checkpoint file>
   

Please refer to the code for advance configurations.