Overview
Benjamin Choi, who works on autonomous vehicles at DSO National Laboratories, will be sharing on using Bayes filters and probabilistic representations of the world for robust self-driving components such as localisation against a map and data fusion from multiple sensors.
Ben develops and implements algorithms for autonomy in unmanned ground vehicles at DSO National Laboratories. He studied Computer Science at CMU and there first became acquainted with the joys and frustrations of working with robots.
Although his work is now robotics-focused, he remains interested in parallel algorithms, compilers, machine learning, and software engineering that can serve society. He also enjoys teaching and mentoring (especially if it has anything to do with programming).
In his presentation, Ben will introduce Bayes filters and probabilistic representations of the world, and show how they are used to build components critical for robust self-driving such as localisation against a map and data fusion from multiple sensors (He will be drawing source material from publications from the Stanford Autonomous Driving Team and entrants in the DARPA challenges).
