Dynamic Kalman filter-based velocity tracker for Intelligent vehicle

In the domain of autonomous vehicles, accurate modeling of the ever-changing dynamic environments is achieved using the DATMO (Detection And Tracking of Moving Objects) algorithm. This method uses input from various types of sensors and estimates their position and velocity using Kalman Filters with the integration of constant velocity model. Kalman Filters have increased in popularity as a part of robotics-related research in recent decades. The most promising applications of Kalman Filters can be seen in velocity estimation and robot localization. This paper proposes an implementation that uses point cloud data to predict the position and velocity of moving objects if and when they are detected. As demonstrated in the experimental results, the Kalman Filter accurately determines these quantities using noisy input point could data. We present a dynamic implementation, improved from a previously static implementation, for obstacle tracking. It can be helpful in automatic parking in vehicles and making decisions related to obstacle avoidance.