To navigate in an environment safely and autonomously, robots must accurately estimate where obstacles are and how they move. Instead of using expensive traditional 3D sensors, we explore the use of a much cheaper, faster, and higher resolution alternative: programmable light curtains. Light curtains are a controllable depth sensor that sense only along a surface that the user selects. We adapt a probabilistic method based on particle filters and occupancy grids to explicitly estimate the position and velocity of 3D points in the scene using partial measurements made by light curtains. The central challenge is to decide where to place the light curtain to accurately perform this task. We propose multiple curtain placement strategies guided by maximizing information gain and verifying predicted object locations. Then, we combine these strategies using an online learning framework. We propose a novel self-supervised reward function that evaluates the accuracy of current velocity estimates using future light curtain placements. We use a multi-armed bandit framework to intelligently switch between placement policies in real time, outperforming fixed policies. We develop a full-stack navigation system that uses position and velocity estimates from light curtains for downstream tasks such as localization, mapping, path-planning, and obstacle avoidance. This work paves the way for controllable light curtains to accurately, efficiently, and purposefully perceive and navigate complex and dynamic environments. Project website: https://siddancha.github.io/

翻译：为了实现安全自主的环境导航，机器人必须精确估计障碍物的位置及其运动状态。我们探索使用可编程光帘这一更廉价、更快且分辨率更高的替代方案，替代昂贵传统3D传感器。光帘是一种可控深度传感器，仅沿用户选择的表面进行感知。我们基于粒子滤波器和占据网格的概率方法，利用光帘的部分测量值显式估计场景中3D点的位置和速度。核心挑战在于如何选择光帘放置位置以准确完成该任务。我们提出多种光帘放置策略，通过最大化信息增益和验证预测目标位置来指导决策，并利用在线学习框架整合这些策略。我们设计了一种新颖的自监督奖励函数，通过后续光帘放置评估当前速度估计的准确性。基于多臂老虎机框架，我们能够实时智能切换放置策略，其性能优于固定策略。我们开发了全栈导航系统，将光帘的位置和速度估计用于定位、建图、路径规划和避障等下游任务。这项工作为可控光帘准确、高效、有目的地感知和导航复杂动态环境奠定了基础。项目网站：https://siddancha.github.io/