Skip to content

Latest commit

 

History

History
126 lines (91 loc) · 5.05 KB

README.md

File metadata and controls

126 lines (91 loc) · 5.05 KB
Raw

AirVO

An Illumination-Robust Point-Line Visual Odometry

pipeline

AirVO is an illumination-robust and accurate stereo visual odometry (VO) system based on point and line features. It is a hybrid VO system that combines the efficiency of traditional optimization techniques with the robustness of learning-based methods. To be robust to illumination variation, we introduce both the learning-based feature extraction (SuperPoint) and matching (SuperGlue) method to the system. Moreover, We propose a new line processing pipeline for VO that associates 2D lines with learning-based 2D points on the image, leading to more robust feature matching and triangulation. This novel method enhances the accuracy and reliability of VO, especially in illumination-challenging environments. By accelerating CNN and GNN parts using Nvidia TensorRT Toolkit, our point feature detection and matching achieve more than 5× faster than the original codes. The system can run at a rate of about 15Hz on the Nvidia Jetson AGX Xavier (a low-power embedded device) and 40Hz on a notebook PC.

Authors: Kuan Xu, Yuefan Hao, Shenghai Yuan, Chen Wang, and Lihua Xie

Related Papers

AirVO: An Illumination-Robust Point-Line Visual Odometry, Kuan Xu, Yuefan Hao, Shenghai Yuan, Chen Wang and Lihua Xie, arXiv preprint arXiv:2212.07595, 2022. PDF.

If you use AirVO, please cite:

@article{xu2022airvo,
  title={AirVO: An Illumination-Robust Point-Line Visual Odometry},
  author={Xu, Kuan and Hao, Yuefan and Wang, Chen and Xie, Lihua},
  journal={arXiv preprint arXiv:2212.07595},
  year={2022}
}

Demos

UMA-VI dataset

UMA-VI dataset contains many sequences where images may suddenly darken as a result of turning off the lights. Here are demos on two sequences.

umauma_traj

OIVIO dataset

OIVIO dataset collects data in mines and tunnels with onboard illumination.

oivio oivio_traj

Live demo with realsense camera

We also test AirVO on sequences collected by Realsense D435I in the environment with continuous changing illumination.

realsense

Live demo in a dark environment.

We collect the data in a factory.

dark

More

Video demo

Efficiency Comparison

The evaluation is performed on the Nvidia Jetson AGX Xavier (2018), a low-power embedded platform with an 8-core ARM v8.2 64-bit CPU and a lowpower 512-core NVIDIA Volta GPU. The resolution of the input image sequence is 640 × 480. We extract 200 points and disabled the loop closure, relocalization and visualization part for all algorithms.

efficiency

Test Environment

Dependencies

  • OpenCV 4.2
  • Eigen 3
  • G2O
  • TensorRT 8.4
  • CUDA 11.6
  • python
  • onnx
  • ROS noetic
  • Boost
  • Glog

Docker (Recommend)

docker pull xukuanhit/air_slam:v1
docker run -it --env DISPLAY=$DISPLAY --volume /tmp/.X11-unix:/tmp/.X11-unix --privileged --runtime nvidia --gpus all --volume ${PWD}:/workspace --workdir /workspace --name air_slam xukuanhit/air_slam:v1 /bin/bash

Data

The data should be organized using the Automous Systems Lab (ASL) dataset format just like the following:

dataroot
├── cam0
│   └── data
│       ├── 00001.jpg
│       ├── 00002.jpg
│       ├── 00003.jpg
│       └── ......
└── cam1
    └── data
        ├── 00001.jpg
        ├── 00002.jpg
        ├── 00003.jpg
        └── ......

Build

    cd ~/catkin_ws/src
    git clone https://github.com/xukuanHIT/AirVO.git
    cd ../
    catkin_make
    source ~/catkin_ws/devel/setup.bash

Run

OIVIO Dataset

roslaunch air_vo oivio.launch

UMA-VI Dataset

roslaunch air_vo uma_bumblebee_indoor.launch

Euroc Dataset

roslaunch air_vo euroc.launch

Acknowledgements

We would like to thank SuperPoint and SuperGlue for making their project public.