ARM-VO

Authors: Zana Zakaryaie Nejad and Ali Hosseininaveh

ARM-VO is an efficient monocular visual odometry algorithm designed for ARM processors. It uses NEON C intrinsics and multi-threading to accelerate keypoint detection and tracking. Check this video to see the performance on Raspberry Pi 3 and Odroid XU4.

Dependencies

• Cmake
• OpenCV (built with TBB)
• ROS (Optional)

How to build?

git clone https://github.com/zanazakaryaie/ARM-VO.git
cd ARM-VO
cmake .
make

Test on KITTI dataset

Download the odometry dataset from here. Open a terminal and type:

./ARM_VO pathToData paramsFileName

ROS (Optional)

The ros node subscribes to "kitti/image" topic and publishes "arm_vo/pose" which is of type geometry_msg::Pose. To use the ROS node: Open a terminal and type:

cd ROS
catkin_make
source devel/setup.bash
rosrun ARM_VO ARM_VO paramsFileName

Now, open another terminal and type:

cd ROS
source devel/setup.bash
rosrun ARM_VO ImagePublisher pathToData

Limitations

• ARM-VO recovers the scale if the camera height and pitch angle are provided. Thus, it is not applicable for drones or hand-held cameras.

• The algorithm detects small-inter frame translations and pure rotations using GRIC but it doesn't decompose the estimated homography matrix. Track is lost if the camera rotates too much without translation.

Notes

• If you get low FPS, check your power adapter. Raspberry Pi 3 runs ARM-VO at 8 frames per second (averagelly) if powered up with a 5V-2A adapter.

• If you use ARM-VO in an academic work, please cite:
  

@article{nejad2019arm,
  title={ARM-VO: an efficient monocular visual odometry for ground vehicles on ARM CPUs},
  author={Nejad, Zana Zakaryaie and Ahmadabadian, Ali Hosseininaveh},
  journal={Machine Vision and Applications},
  volume={30},
  number={6},
  pages={1061--1070},
  year={2019},
  publisher={Springer}
}

• ARM-VO is a part of six-wheel surveying robot project named MOOR.

• Here is a Python wrapper for ARM-VO.