Title: CNN-based Single Image Obstacle Avoidance on a Quadrotor Speaker: Dr. Prunarjay Chakravarty Time: Tuesday, 27 Sep, 5pm Venue: RM101 Abstract: This talk will be about the use of a single forward facing camera for obstacle avoidance on a quadrotor. A ConvolutionalNeural Network(CNN) is trained for estimating depth from a single image. The depth map is then fed to a behaviour arbitration based control algorithm that steers the quadrotor away from obstacles. Experiments conducted demonstrate the use of single image depth for controlling the quadrotor in both simulated and real environments. Speaker's Bio: Dr. Chakravarty completed his PhD from Monash University, Australia in 2010, where he worked on using mobile robots for surveillance applications. He subsequently worked for Sensen Networks, a start-up where he led a team that developed a mobile parking enforcement system - a car equipped with a computer vision system that automatically geo-tags number plates of vehicles that are parked illegally or have overstayed their allotted parking. At Sensen, he was also involved in a video surveillance project at Abu Dhabi airport, which involved the development and setting up of the largest video analytics system in the world at the time - face and number plate recognition on feeds from 1800 cameras at the airport. After 4 years at the startup, he moved to KU Leuven, Belgium, where he is currently employed as a post-doctoral researcher. He works on several computer vision projects including video diarization and using vision for automating the flight of quadrotor helicopters. He has publications in both Robotics and Computer Vision conferences like ICRA, IROS, ICMI and ECCV.