Robust perception algorithms for fast and agile navigation

Date:  11/17/2022

Speaker:   Varun Murali

Location: 122 Gates Hall and Zoom

Time: 2:40 p.m.-3:30 p.m.

AbstractIn this talk we explore algorithms for robust visual navigation at operational speeds. Visual inertial navigation at operational speeds is a challenging problem for robotic vehicles.With a camera, inertial measurement unit (IMU) pairing being ubiquitous to most consumer electronics, they form an ideal pairing for applications on the edge and have found applications ranging from large-scale search and rescue, autonomous driving to home robots such as robotic vacuum cleaners. In general, the navigation problem for robots can be written in the form of the sense-think-act framework for autonomy. The “sensing” part is typically performed in this context as bearing measurements to visually salient locations in the environment; the “planning” part then uses the estimate of the ego-state from the sensors and produces a (compactly represented) trajectory from the current location to the goal. Finally, the “act” or controller follows the plan. This division leaves several interesting problems at the intersection of the parts of the framework. For instance, consider the problem of navigating in a relatively unknown environment; if the future percepts are not carefully planned, it is possible to enter a room with very few visual features that degrade the quality of state estimation, which in turn can result in poor closed-loop performance. To this end, we explore the joint problem of perception and planning in a unified setting and show that this framework results in robust trajectory tracking.

Bio: Varun is currently a PhD candidate at MIT working on decision making under uncertainty for agile navigation. Previously, he was a Computer Scientist with the Computer Vision Technology group at SRI International in Princeton, New Jersey, USA working on GPS denied localization algorithms using low cost sensors. Varun received his bachelor’s degree in Electronics and Communications Engineering from the University of Kent at Canterbury, UK. He also received master’s degrees in Electrical and Computer Engineering and Computer Science with a specialization in computational perception and robotics from the Georgia Institute of Technology. He has also held positions at Dynamic Load Monitoring, Southampton, UK and BMW, Munich, Germany. He enjoys research roles and has been involved in different areas of research in robotics and computer vision, including work on joint perception and planning, semantic localization, guaranteed safe navigation and smooth control for wearable robotics.