Abstract: The development of robust and lightweight perception algorithms is crucial towards the deployment of robotics technologies, ranging from self-driving cars to micro aerial vehicles. Perception algorithms are responsible for interpreting sensor data into a coherent world representation, which can be used to support navigation and decision-making. Perception needs to be lightweight, to cope with limited on-board computation; moreover, it needs to produce certifiably correct results, in the face of large measurement noise and outliers.
In this talk, I present my work on lightweight and robust robot perception, using agile navigation of micro aerial vehicles as a motivating application. I start by drawing connections between robot perception and optimization, and show that a large class of geometric problems in robotics and computer vision can be cast as a nonconvex optimization problem with variables living on manifold. Then I consider an instance of this nonconvex problem, and present a convex relaxation that is able to recover the exact global solution of the nonconvex problem in a noise regime that encompasses most applications in robotics and computer vision. Besides being certifiably correct and robust to large noise, our convex relaxation is lightweight, entailing a computational cost that is an order of magnitude smaller than standard iterative solvers. After discussing robot perception, I provide a brief overview of our recent work on lightweight sensing. I consider the case in which a small robot does not have sufficient payload to carry a standard depth sensor, and it has to reconstruct the geometry of the environment from a sparse set of noisy depth measurements. Also in this case, I show that the use of convex relaxations, akin the ones used in compressive sensing, enables reconstruction from sparse data. I conclude by proposing few ideas to scale down perception to miniaturized platforms, such as nano and pico aerial vehicles, where sensing and computation are subject to strict payload and power constraints.
Bio: Luca Carlone is a research scientist in the Laboratory for Information and Decision Systems at the Massachusetts Institute of Technology. Before joining MIT, he was a postdoctoral fellow at Georgia Tech (2013-2015), and a visiting researcher at the University of California Santa Barbara (2011). He got his Ph.D. from the Polytechnic University of Turin, Italy, in 2012. His research interests include nonlinear estimation, numerical and distributed optimization, computer vision and probabilistic inference applied to sensing, perception, and control of single and multi robot systems. He published more than 60 papers on international journals and conferences, including a best paper award finalist at RSS 2015 and a best paper award winner at WAFR 2016.
Host: Luca Daniel