AVLMaps

Audio Visual Language Maps for Robot Navigation

Chenguang Huang, Oier Mees, Andy Zeng, Wolfram Burgard

We present AVLMAPs (Audio Visual Language Maps), an open-vocabulary 3D map representation for storing cross-modal information from audio, visual, and language cues. When combined with large language models, AVLMaps consumes multimodal prompts from audio, vision and language to solve zero-shot spatial goal navigation by effectively leveraging complementary information sources to disambiguate goals.

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Quick Start

Try AVLMaps creation and landmark indexing in .

Setup Environment

To begin on your own machine, clone this repository locally

git clone https://github.com/avlmaps/AVLMaps.git

Install requirements:

$ conda create -n avlmaps python=3.8 -y  # or use virtualenv
$ conda activate avlmaps
$ conda install jupyter -y
$ cd AVLMaps
$ bash install.bash

Download Checkpoints

You can download the AudioCLIP and LSeg checkpoints with the following command:

bash download_checkpoints.bash

Generate Dataset

To build AVLMaps for simulated environments, we manually collected RGB-D videos among 10 scenes in Habitat simulator with Matterport3D dataset. We provide script and pose meta data to generate the RGB-D videos. We also collect 20 sequences of RGB videos with poses for each scene and insert audios from ESC-50 dataset to create audio videos. Please follow the next few steps to generate the dataset.

Download ESC50 dataset

We need to download the source ESC50 audio dataset with the following command. For more information, please check the official repo: https://github.com/karolpiczak/ESC-50.

wget https://github.com/karoldvl/ESC-50/archive/master.zip -P ~/
unzip ~/master.zip -d <target_dir>

The extracted ESC-50 dataset is under the directory <target_dir>/ESC-50-master. You need to modify the paths in config/data_paths/default.yaml:

set esc50_meta_path to <target_dir>/ESC-50-master/meta/esc50.csv
set esc50_audio_dir to <target_dir>/ESC-50-master/audio

Download Matterpot3D dataset

Please check Dataset Download, sign the Terms of Use, and send to the responsible person to request the Matterport3D mesh for the use in Habitat simulator. The return email will attach a python script to download the data. Copy and paste the script to a file ~/download_mp.py. Run the following to download the data:

cd ~
# download the data at the current directory
python2 download_mp.py -o . --task habitat
# unzip the data
unzip v1/tasks/mp3d_habitat.zip
# the data_dir is mp3d_habitat/mp3d

Modify the paths in config/data_paths/default.yaml:

Change the habitat_scene_dir to the downloaded Matterport3D dataset ~/mp3d_habitat/mp3d.

The structure of the habitat_scene_dir looks like this


  # the structure of the habitat_scene_dir looks like this
  habitat_scene_dir
    |-5LpN3gDmAk7
    |   |-5LpN3gDmAk7.glb
    |   |-5LpN3gDmAk7_semantic.ply
    |   |-...
    |-gTV8FGcVJC9
    |   |-gTV8FGcVJC9.glb
    |   |-gTV8FGcVJC9_semantic.ply
    |   |-...
    |-jh4fc5c5qoQ
    |   |-jh4fc5c5qoQ.glb
    |   |-jh4fc5c5qoQ_semantic.ply
    |   |-...
    ...

Download and Generate AVLMaps Dataset

Configure the config/generate_dataset.yaml

* Change the value for `defaults/data_paths` in `config/generate_dataset.yaml` to `default`. * Change the `avlmaps_data_dir` to the where you want to download the dataset * Change `data_cfg.resolution.w` and `data_cfg.resolution.h` to adjust the resolution of the generated rgb, depth, and semantic images. * Change `rgb`, `depth`, and `semantic` to `true` to generate corresponding data, and to `false` to ignore corresponding data. * Change `camera_height` to change the height of camera relative to the robot base

Run the following command to download and generate the dataset. The generated dataset takes around 150GB disk space.

# go to <REPO_ROOT>/dataset of this repository
python dataset/generate_dataset.py

After the data generation, the data structure will look like the following


# the structure of the avlmaps_data_dir will look like this
avlmaps_data_dir
├── 5LpN3gDmAk7_1
│   ├── poses.txt
│   ├── audio_video
│   │   ├── 000000
│   │   │   ├── meta.txt
│   │   │   ├── poses.txt
│   │   │   ├── output.mp4
│   │   │   ├── output_level_1.wav
│   │   │   ├── output_level_2.wav
│   │   │   ├── output_level_3.wav
│   │   │   ├── output_with_audio_level_1.mp4
│   │   │   ├── output_with_audio_level_2.mp4
│   │   │   ├── output_with_audio_level_3.mp4
│   │   │   ├── range_and_audio_meta_level_1.txt
│   │   │   ├── range_and_audio_meta_level_2.txt
│   │   │   ├── range_and_audio_meta_level_3.txt
│   │   │   ├── rgb
│   │   │   |   ├── 000000.png
│   │   │   |   ├── ...
│   │   ├── 000001
│   │   ├── ...
│   ├── depth
│   │   ├── 000000.npy
│   │   ├── ...
│   ├── rgb
│   │   ├── 000000.png
│   │   ├── ...
│   ├── semantic
│   │   ├── 000000.npy
│   │   ├── ...
├── gTV8FGcVJC9_1
│   ├── ...
├── jh4fc5c5qoQ_1
│   ├── ...
...

The details of the structure of data are explained in the dataset README.

Create a AVLMap with the Generated Dataset

Change the value for defaults/data_paths in config/map_creation_cfg.yaml to default.
Change the habitat_scene_dir and avlmaps_data_dir in config/data_paths/default.yaml according to the steps in the Generate Dataset section above.
Run the following command to build the VLMap
```
cd application
python create_map.py
```

Config the Created AVLMap

Change the scene you want to generate VLMap for by changing scene_id (0-9) in config/map_creation_cfg.yaml
Customize the map by changing the parameters in config/params/default.yaml
- Change the resolution of the map by changing cs (cell size in meter) and gs (grid size)
Customize the camera pose and base pose by changing config/vlmaps.yaml. Change the pose_info section.
- pose_type means the type of poses stored in poses.txt files. Currently we only support mobile_base which means the poses are the poses for the base. But you can implement camera if you want.
- camera_height means the camera height relative to the base. Change it if you set different camera height when you generate the dataset.
- base2cam_rot means the row-wise flattened rotation matrix from robot base to the camera coordinate frame (z forward, x right, y down).
- base_forward_axis, base_left_axis, base_up_axis: your robot base coordinate. They mean what is the coordinate of the forward unit vector [1, 0, 0] projected into your robot base frame, the coordinate of the left unit vector [0, 1, 0] projected into your robot base frame, the coordinate of the upward unit vector [0, 0, 1] projected into your robot base frame.
Other settings in config/vlmaps.yaml
- cam_calib_mat is the flattened camera intrinsics matrix
- depth_sample_rate: we only back project randomly sampled h * w / depth_sample_rate pixels at each frame

Index an AVLMap

Change the value for defaults/data_paths in config/map_indexing_cfg.yaml to default.
Change the habitat_scene_dir and avlmaps_data_dir in config/data_paths/default.yaml according to the steps in the Generate Dataset section above.
Run the following command to index a VLMap you built
```
cd application
python index_map.py
```
You will be asked to input a number to indicate what kind of indexing you want to perform.

Index Object: input an object category name to generate the heatmap.

Index Sound: you will see a top-down map showing GT positions of inserted sounds. Input a sound name to generate the heatmap.

Index Area: input an area name like "kitchen", "bathroom" to generate the heatmap.

Index Image: you will see a top-down map. Select two points on the map to get the image position and the pointing direction. Then you will see the selected pose on the top-down map and the query image at that pose. The heatmap for the query image is subsequently generated.

Configure the Indexing

Change the file config/map_indexing_cfg.yaml
- decay_rate: set the heatmap decay rate. When it is smaller, the transition of the heat is clearer and covers larger area.
- image_query_cfg:
- set the camera height with camera_height
- set the image resolution with resolution.w and resolution.h
- set save_query_image to True to save the selected query image in the interactive top-down map.

Citation

If you find the dataset or code useful, please cite:

@inproceedings{huang23avlmaps,
              title={Audio Visual Language Maps for Robot Navigation},
              author={Chenguang Huang and Oier Mees and Andy Zeng and Wolfram Burgard},
              booktitle={Proceedings of the International Symposium on Experimental Robotics (ISER)},
              year={2023},
              address = {Chiang Mai, Thailand}
          }

License

MIT License

Acknowledgement

We extend our heartfelt gratitude to the authors of the projects listed below for generously sharing their code with the public, thus greatly facilitating our research on AVLMaps:

Your contribution is invaluable to our work, and we deeply appreciate your commitment to advancing the field.

avlmaps / AVLMaps

readme