This package provides basic state (i.e. linear and angular position, velocity, acceleration) estimation functionality using optical flow data from a camera rigidly mounted to the chassis of the robot.
The camera data can be fused with IMU data using an Extended Kalman Filter to improve accuracy. Keep reading for specifics.
to start the visual odometery hack node
roslaunch mono_vo_ros start_vo.launch
to start the visual odometery node using Essential matrix method
roslaunch mono_vo_ros start_vo_mono.launch
Running the code should automatically display one cv window.
clone the repository and catkin_make
git clone https://github.com/chrissunny94/mono_vo_ros
catkin_make
The below are the main scripts and what it does individually
flow.cpp
Extracts features, calculates optical flow, and performs perspective transform on a live camera feed. Publishes a geometry_msgs/Twist message to /optical_flow/twist which contains twist data in the form of velocity in x, y, z, roll, pitch, and yaw.
twist_data.py
Converts output of flow.cpp from a geometry_msgs/Twist message to a nav_msgs/Odometry message which contains measurement covariances in addition to the original Twist data. This message is published to the /optical_flow topic.
