arm-vo's Introduction

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}
}