Opening the Doors of (Robot) Perception: Towards Certifiable Spatial Perception Algorithms and Systems
Luca Carlone
MIT
February 11, 2022
Spatial perception —the robot’s ability to sense and understand the surrounding environment— is a key enabler for autonomous systems operating in complex environments, including self-driving cars and unmanned aerial vehicles. Recent advances in perception algorithms and systems have enabled robots to detect objects and create large-scale maps of an unknown environment, which are crucial capabilities for navigation, manipulation, and human-robot interaction. Despite these advances, researchers and practitioners are well-aware of the brittleness of existing perception systems, and a large gap still separates robot and human perception. This talk presents our latest results on the design of the next generation of robot perception systems and algorithms. The first part of the talk discusses spatial perception systems and motivates the need for high-level 3D scene understanding for robotics. I introduce early work on metric-semantic mapping (Kimera) and novel hierarchical representations for 3D scene understanding (3D Dynamic Scene Graphs). Then, I present recent results on the development of Hydra, the first real-time spatial perception system that builds 3D scene graphs of the environment in real-time and without human supervision. The second part of the talk focuses on perception algorithms and draws connections between robustness of robot perception and global optimization. I present an overview of our certifiable perception algorithms, a novel class of algorithms that is robust to extreme amounts of noise and outliers and affords performance guarantees. I discuss the theoretical implications of our certifiable algorithms and showcase applications to vehicle pose and shape estimation in self-driving scenarios.
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