ProxMaP

This repository contains the code for ProxMaP: Proximal Occupancy Map Prediction for Efficient Indoor Robot Navigation by Vishnu D. Sharma, Jingxi Chen, and Pratap Tokekar.

Data Generation

Data is generated with AI2THOR simulator. Create directories for saving data and change the paths at Line 14-16 in data_generation.py. Then Run python data_generation.py. This will generate .npy files, and a desceription file named updated_description_ang0.csv, which will be used by dataloader.

Download data

Already generated data is avaialable for download as a zip file here.

Training

The code for ProxMaP is deveoped using UNet implementation by milesial.

For training the model, create directories named saved_models (for saving models as .pth files) and tblogdir (for saving tensorboard logs). Then run the following command python train_classification.py --epoch 500 --batch-size 8 --learning-rate 0.01 --validation 10 --loss-function 'crossent' --logdir ./tblogdir/.

You can look at other training parameters by running python train_classification.py --help.

Pre-trained Model

Pretrained classification model is avialable for download here.

Regression Model Training

The regression models can be run with similar command as above using the file named train_regression.py.

Testing

For testing the model, run python test_classification.py --model-path ./saved_models/classical_model.pth --batch-size 8 --device gpu --show with appropriate model name.

Regression Model Testing

The regression models can be run with similar command as above using the file named testregression.py.

File Description

Data generation

• data_generation.py: File to generate dataset. This directory already includes data, so you don't need to run it.
• helper_v3.py: Helper code based on AI2THOR for data geenration
• updated_description_ang0.csv: File containing metatdata for dataset (numpy files)

Model training

• train_classification.py: File to train the classification model
• test_classification.py: File to test the classification model
• train_regression.py: File to train the regression model
• test_regression.py: File to test the regression model
• commands.txt: Shows example training and test command

Citing

If you use our code or data, please cite our work with following:

@article{sharma2023proxmap,
  title={ProxMaP: Proximal Occupancy Map Prediction for Efficient Indoor Robot Navigation},
  author={Sharma, Vishnu Dutt and Chen, Jingxi and Tokekar, Pratap},
  journal={arXiv preprint arXiv:2305.05519},
  year={2023}
}