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/projects/active-velocity-estimation/

翻译：为了让机器人在环境中安全自主地导航，必须准确估计障碍物的位置及其运动方式。我们探索使用一种更廉价、更快、分辨率更高的替代方案——可编程光幕，替代昂贵的传统3D传感器。光幕是一种可控深度传感器，仅沿用户选择的曲面进行感知。我们基于粒子滤波和占用网格，提出了一种概率方法，利用光幕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幕的放置位置以准确完成该任务。我们提出了多种光幕放置策略，这些策略以最大化信息增益和验证预测物体位置为指导。然后，我们通过在线学习框架将这些策略结合起来。我们提出了一种新颖的自监督奖励函数，利用未来的光幕放置来评估当前速度估计的准确性。我们采用多臂老虎机框架在策略之间实时智能切换，其性能优于固定策略。我们开发了一套全栈导航系统，将光幕的位置和速度估计用于定位、地图构建、路径规划和避障等下游任务。这项工作为可控光幕精确、高效且有针对性地感知和导航复杂动态环境铺平了道路。项目网站：https://siddancha.github.io/projects/active-velocity-estimation/