Self Driving Auto-Labeler

This is a custom auto-ground-truthing stack utilizing deep learning (segmentation), visual odometry, sensor data and Kalman filter to generate a path and road edges It uses data from Carla autonomous driving simulator

Sensors to extract data from:

• Gyroscope/IMU
• GPS/GNSS (the 2 preceding sensors are not needed for simulation data, but for real world)
• camera => visual odometry
• steering wheel angle sensor

Libraries used (or to be used in the future):

• Laika for GNSS
• Rednose for Kalman
• SegNet for segmentation

TODO:

• optimize semantic segmentation
• extract labels from segmented images using digital image processing
• detect poses instead of points (Rt matrix) (optional)
• implement visual SLAM
• implement sensor fusion using Kalman Filters