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https://github.com/MAPS-Lab/OdomBeyondVision
https://github.com/MAPS-Lab/OdomBeyondVision
Last synced: about 1 month ago
JSON representation
- Host: GitHub
- URL: https://github.com/MAPS-Lab/OdomBeyondVision
- Owner: MAPS-Lab
- License: mit
- Created: 2022-03-01T05:20:34.000Z (almost 3 years ago)
- Default Branch: main
- Last Pushed: 2022-09-24T06:29:47.000Z (about 2 years ago)
- Last Synced: 2024-08-02T21:38:48.487Z (4 months ago)
- Language: Python
- Size: 4.8 MB
- Stars: 51
- Watchers: 5
- Forks: 5
- Open Issues: 6
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- awesome-radar-perception - Website
README
# OdomBeyondVision: An Indoor Multi-modal Multi-platform Odometry Dataset Beyond the Visible Spectrum
Watch the video:
[
](https://youtu.be/EbjNoZcZpzQ)
## Abstract
We present the **OdomBeyondVision** dataset, a comprehensive indoor odometry dataset containing the data from both emerging and traditional navigation sensors on diverse platforms. The dataset features:
- The first indoor dataset to simultaneously cover the mmWave and LWIR thermal spectrum.
- \>10km sequences on UGV, UAV and handheld platforms in various indoor scenes and illumination conditions.
- Extrinsic calibration parameters for all platforms and intrinsic parameters for all optic sensors, accompanied by several plug-and-play codes for developing multimodal odometry systems with our dataset.
## Paper
The paper describing the dataset can found [here](https://arxiv.org/abs/2206.01589).
To cite this dataset:
```
@misc{https://doi.org/10.48550/arxiv.2206.01589,
doi = {10.48550/ARXIV.2206.01589},
url = {https://arxiv.org/abs/2206.01589},
author = {Li, Peize and Cai, Kaiwen and Saputra, Muhamad Risqi U. and Dai, Zhuangzhuang and Lu, Chris Xiaoxuan and Markham, Andrew and Trigoni, Niki},
keywords = {Robotics (cs.RO), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {OdomBeyondVision: An Indoor Multi-modal Multi-platform Odometry Dataset Beyond the Visible Spectrum},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution 4.0 International}
}
```
## Dataset
The full dataset in the format of Rosbags can be downloaded from [Google Drive](https://drive.google.com/drive/folders/1cJ4w3Dj21EMIox2ZSx7TSdzflxNHhTVL?usp=sharing).
Information for these sequences is recorded on [Google Sheet](https://docs.google.com/spreadsheets/d/1n2KkD_vjE7b5-2_qaq-sq0c-cB4JdQMBo3zMRFuTT_w/edit?usp=sharing).
**Relevant ROS topics**
```
/camera/color/image_raw: RGB image from D435/L515
/camera/depth/image_rect_raw: depth image from D435/L515
/flir_boson/image_raw: thermal image from FLIR Boson
/velodyne_points: point cloud data from Velodyne LiDAR
/imu/data: IMU data from Xsnes MTi
/odom: wheel odometry on UGV
/mmWaveDataHdl/RScan_left_scan: left radar on UGV
/mmWaveDataHdl/RScan_middle_scan: central radar on UGV or Handheld
/mmWaveDataHdl/RScan_right_scan: right radar on UGV
/vrpn_client_node/X500_pglite/pose: Vicon pose on UAV
/ti_mmwave/radar_scan_pcl_0: radar on UAV
```
## Toolkit
Dataset toolkits are provided, to:
- Extract raw data from rosbags;
- Preprocess radar data fro milliEgo training;
- Package training data for milliEgo and DeepTIO.
Details can be seen [here](./toolkit/README.md).
## Calibration
Calibration results for extrinsic parameters and intrinsic parameters are provided [here](./calibration).
## Benchmark
Training, testing and evaluation for milliEgo and DeepTIO are provided [here](./benchmark/).
Pre-trained models for milliEgo and DeepTIO on all 3 platforms can be downloaded from [Google Drive](https://drive.google.com/drive/folders/1cJ4w3Dj21EMIox2ZSx7TSdzflxNHhTVL?usp=sharing).
## Acknowledgement
The data collected on AGV and handheld platforms are supported by Amazon Web Services via the Oxford-Singapore Human-Machine Collaboration Programme and EPSRC ACE-OPS (EP/S030832/1). The AGV and handheld sequences are collected at the University of Oxford.