When: 5.5.2021 at 15:30
Abstract: Machine perception is the process of constructing a model of an embodied agent’s environment from raw sensory data. This capability is essential for mobile robots, supporting such core functions as planning, navigation, and control. However, many fundamental machine perception tasks (e.g. navigation) require the solution of a high-dimensional nonconvex estimation problem, which is computationally intractable in general. This computational complexity presents a serious obstacle to the development of practical and reliable machine perception methods suitable for real-time robotics applications. To address this challenge, in this talk we present a novel class of certifiably correct algorithms that are capable of efficiently solving generally-intractable robotic perception problems in many practical settings. In brief, these methods are based upon a (convex) semidefinite relaxation whose minimizer we prove provides an exact (globally optimal) solution to the original estimation problem under moderate measurement noise; moreover, whenever exactness obtains, it is possible to verify this fact a posteriori, thereby certifying the correctness (global optimality) of the recovered estimate. We illustrate the design of this class of methods using the fundamental problem of robotic mapping as a running example, culminating in the presentation of SE-Sync, the first practical method provably capable of recovering correct (globally optimal) map estimates. Finally, we conclude with a discussion of open questions and future research directions.
You can see the seminar Here