State Estimation and Localization for Self-Driving Cars

Run

• Simply python3 to execute the code, preferably using conda environment

Algorithm

Error-State Kalman Filter for State Estimation and Localization

• Check the course's Final Project, and src code
• Using IMU and GNSS data as measurement input data
• Using error-state kinematics model as the motion/measurement model
• Using quaternion kinematics as the rotation process update
• Implement the algorithm in Python3
• Sample pictures for the results

Reference

Websites

• Coursera website for this course: State Estimation and Locaization
• Coursera website for the other courses from University of Toronto about Self-driving Car topics

Papers & Textbooks

• Quaternion kinematics for the error-state Kalman filter, Sola, 2017
• Quaternions and Rotations, Jia, 2013