We present a comprehensive design and implementation for a micro aerial vehicle (MAV) that is able to perform 3D autonomous navigation and obstacle avoidance in cluttered and realistic unknown environments without the aid of GPS and other external sensors or markers.
Basalt Backend Walkthrough Overview & Class Names The backend of the VIO framework is contained with the base class VioEstimatorBase (defined in vio_estimator.h) which is the parent class of two type of the estimator possible:
Front End The front end include two types of optical flow: frame to frame optical flow and patch optical frame.
Initialization The front end is initialized in
basalt::OpticalFlowFactory::getOpticalFlow(vio_config, calib) func param: vio_config: parameters in config.
Visual-Inertial Odometry System Overview The ekf_sensor_fusion package performs loose-coupled sensor fusion from stereo camera’s visual odometry and IMU’s inertial measurement to produce UAV pose estimation.
The filter is formulated to take in body-frame velocity measurement from the visual odometry module (ZED camera), instead of the pose measurement with respect to the initial body-frame.