A Cpp Version of HF-NET

9ms in GeForce GTX 2080 Ti

15-20ms in GeForce GTX 1660s 6G

Since the origin HF-NET based on Python3, May be it is a little difficult integrate the code with your Cpp projects(some SLAM system). To run the hfnet_ros package, you need install the tensorflow from the source. For convience, you can using the docker image here to run this package.

This package allows to:

• Extract feature detectors and descriptors on standard image sequence
• Integrate other slam system which written in cpp.
• Combine the origin hloc-cpp back-end and make a full cpp projects.
• It takes 15-20ms in GeForce GTX 1660s 6G.

Setup

• python 2.7
• Ubuntu 16.04
• ROS Kinetic
• CUDA = 9.0
• Cudnn = 7.0
• Tensorflow cpp gpu == 1.12 (you need build and install tf1.12 from source, and copy the libs to /usr/local/lib)
• opencv > 3.0

Docker

Build tensorflow from source is annoying, So if you wanna test the code , you can using docker image.

sudo docker pull xinliangzhong/1604-tf1.2cc-cuda9.0-cudnn7.0-ros:latest

sudo docker run --gpus all --name="hfnet-ros" --net=host -it -v /YOUR/PATH:/root/data xinliangzhong/1604-tf1.2cc-cuda9.0-cudnn7.0-ros:latest /bin/bash

How to run

cd /root/data
mkdir -p catkin_ws/src
cd catkin_ws/src
git clone https://github.com/TurtleZhong/hfnet_ros.git

# fetch model
cd hfnet_ros/model/
chmod +x download.sh
./download.sh

catkin_make -DCMAKE_BUILD_TYPE=Release

cd /root/data/catkin_ws/
source devel/setup.bash
roslaunch hfnet_ros hfnet_ros_node.launch

Then you will get this in terminal.

...
detected: 817 pts
IMAGE[480 x 752] cost: 18 ms
...

The results image is publish as ros msg. if your start docker container using --net=host, you can using rqt to view the results.