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
翻译:为了安全地航行未知的环境,机器人必须准确地看到动态障碍。我们不直接用激光雷达传感器测量现场深度,而是要探索使用一个更便宜、更高分辨率的传感器:可编程的光窗帘。光窗帘是可控制的深度传感器,仅能从用户选择的表面感知。我们用光窗帘来估计场景的安全范围:一个将机器人与所有障碍隔开的假设表面。我们显示,产生光窗帘能够感知随机位置(从特定分布中)能够迅速发现有未知物体的场景的安全封套。重要的是,我们为使用随机窗帘探测障碍的可能性提供理论安全保障。我们把随机窗帘与基于机器的学习模型结合起来,以便有效地预测和跟踪安全信封的动。我们的方法准确地估计了安全封套,同时提供概率性安全保障,用以验证机器人感知系统的功效,以探测和避免动态障碍。我们用光窗帘装置对模拟的城市驱动环境和现实世界环境中行人移动的方法进行了评估,并显示我们能够有效和有效地估计安全信封。项目网站: http://qs-abs-hantistrubsilm-tatoim-tatios-taimus-taimus-taimus-tatium