LiDAR and Radar Sensor Fusion using Unscented Kalman Filter

Sensor fusion is the process of combining data from multiple sensors to obtain a more accurate and reliable estimate of the state of a system. In the field of autonomous driving, sensor fusion is essential for achieving robust perception and localization of the vehicle and its surroundings. One of the usual challenges in sensor fusion is how to fuse data from several types of sensors, such as LiDAR and Radar, which have distinctive characteristics and advantages. In this article, I share my experience of implementing a LiDAR and Radar sensor fusion algorithm using an Unscented Kalman Filter (UKF), as part of my capstone project for the Udacity Sensor Fusion Nanodegree. I will explain the basic concepts and steps of the UKF and show the final output of my project written in C++: Multiple Vehicle Tracking using the UKF. <a href="https://medium.com/@nikhilnair8490/lidar-and-radar-sensor-fusion-using-unscented-kalman-filter-5b20de0ab1d1">Read More</a>