Certifiably-Robust Spatial Perception for Robots and Autonomous Vehicles
Date: Friday, May 10, 2019
Time: 2:00 PM to 3:00 PM
Location: 32-D463 (Star)
Event Type: Seminar
Host: Alberto Rodriguez & Sangbae Kim
Contact: Stephen Proulx, email@example.com
Speaker URL: https://lucacarlone.mit.edu/
TALK: Certifiably-Robust Spatial Perception for Robots and Autonomous Vehicles
Abstract: Spatial perception is concerned with the estimation of a world model --that describes the state of the robot and the environment-- using sensor data and prior knowledge. As such, it includes a broad set of robotics and computer vision problems, ranging from object detection and pose estimation to robot localization and mapping. Most perception algorithms require extensive and application-dependent parameter tuning and often fail in off-nominal conditions (e.g., in the presence of large noise and outliers). While many applications can afford occasional failures (e.g., AR/VR, domestic robotics) or can structure the environment to simplify perception (e.g., industrial robotics), safety-critical applications of robotics in the wild, ranging from self-driving vehicles to search & rescue, demand a new generation of algorithms.
In this talk I present recent advances in the design of spatial perception algorithms that are robust to extreme amounts of outliers and afford performance guarantees. I first provide a negative result, showing that a general formulation of outlier rejection is inapproximable: in the worst case, it is impossible to design an algorithm (even “slightly slower” than polynomial time) that approximately finds the set of outliers. While it is impossible to guarantee that an algorithm will reject outliers in worst-case scenarios, our second contribution is to develop certifiably-robust spatial perception algorithms, that are able to assess their performance in every given problem instance. We consider two popular spatial perception problems: Simultaneous Localization And Mapping and 3D registration, and present efficient algorithms that are certifiably-robust to extreme amounts of outliers. As a result, we can solve registration problems where 99% of the measurements are outliers and succeed in localizing objects where an average human would fail.
Bio: Luca Carlone is the Charles Stark Draper Assistant Professor in the Department of Aeronautics and Astronautics at the Massachusetts Institute of Technology, and a Principal Investigator in the Laboratory for Information & Decision Systems (LIDS). He received his PhD from the Polytechnic University of Turin in 2012. He joined LIDS as a postdoctoral associate (2015) and later as a Research Scientist (2016), after spending two years as a postdoctoral fellow at the Georgia Institute of Technology (2013-2015). His research interests include nonlinear estimation, numerical and distributed optimization, and probabilistic inference, applied to sensing, perception, and decision-making in single and multi-robot systems. He is a recipient of the 2017 Transactions on Robotics King-Sun Fu Memorial Best Paper Award, and the best paper award at WAFR 2016.
Created by Stephen Proulx at Wednesday, May 08, 2019 at 8:55 AM.