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To safely navigate unknown environments, robots must accurately perceivedynamic obstacles. Instead of directly measuring the scene depth with a LiDARsensor, we explore the use of a much cheaper and higher resolution sensor:programmable light curtains. Light curtains are controllable depth sensors thatsense only along a surface that a user selects. We use light curtains toestimate the safety envelope of a scene: a hypothetical surface that separatesthe robot from all obstacles. We show that generating light curtains that senserandom locations (from a particular distribution) can quickly discover thesafety envelope for scenes with unknown objects. Importantly, we producetheoretical safety guarantees on the probability of detecting an obstacle usingrandom curtains. We combine random curtains with a machine learning based modelthat forecasts and tracks the motion of the safety envelope efficiently. Ourmethod accurately estimates safety envelopes while providing probabilisticsafety guarantees that can be used to certify the efficacy of a robotperception system to detect and avoid dynamic obstacles. We evaluate ourapproach in a simulated urban driving environment and a real-world environmentwith moving pedestrians using a light curtain device and show that we canestimate safety envelopes efficiently and effectively. Project website:https://siddancha.github.io/projects/active-safety-envelopes-with-guarantees

Active Safety Envelopes using Light Curtains with Probabilistic Guarantees