確率的保証付きライトカーテンによるアクティブ安全エンベロープ
Active Safety Envelopes using Light Curtains with Probabilistic Guarantees
安価で高解像度なプログラマブルライトカーテンを用い、ランダムな感知位置から未知障害物の安全エンベロープを推定し、機械学習で動きを予測・追跡することで確率的な安全保証を提供する手法を提案した。
著者: Siddharth Ancha, Gaurav Pathak, Srinivasa G. Narasimhan, David Held
分類: cs.LG, cs.AI, cs.CV, cs.RO
原文アブストラクト
To safely navigate unknown environments, robots must accurately perceive dynamic obstacles. Instead of directly measuring the scene depth with a LiDAR sensor, we explore the use of a much cheaper and higher resolution sensor: programmable light curtains. Light curtains are controllable depth sensors that sense only along a surface that a user selects. We use light curtains to estimate the safety envelope of a scene: a hypothetical surface that separates the robot from all obstacles. We show that generating light curtains that sense random locations (from a particular distribution) can quickly discover the safety envelope for scenes with unknown objects. Importantly, we produce theoretical safety guarantees on the probability of detecting an obstacle using random curtains. We combine random curtains with a machine learning based model that forecasts and tracks the motion of the safety envelope efficiently. Our method accurately estimates safety envelopes while providing probabilistic safety guarantees that can be used to certify the efficacy of a robot perception system to detect and avoid dynamic obstacles. We evaluate our approach in a simulated urban driving environment and a real-world environment with moving pedestrians using a light curtain device and show that we can estimate safety envelopes efficiently and effectively. Project website: https://siddancha.github.io/projects/active-safety-envelopes-with-guarantees
