In this paper, we consider a Micro Aerial Vehicle (MAV) system teleoperated by a non-expert and introduce a perceptive safety filter that leverages Control Barrier Functions (CBFs) in conjunction with Visual-Inertial Simultaneous Localization and Mapping (VI-SLAM) and dense 3D occupancy mapping to guarantee safe navigation in complex and unstructured environments. Our system relies solely on onboard IMU measurements, stereo infrared images, and depth images and autonomously corrects teleoperated inputs when they are deemed unsafe. We define a point in 3D space as unsafe if it satisfies either of two conditions: (i) it is occupied by an obstacle, or (ii) it remains unmapped. At each time step, an occupancy map of the environment is updated by the VI-SLAM by fusing the onboard measurements, and a CBF is constructed to parameterize the (un)safe region in the 3D space. Given the CBF and state feedback from the VI-SLAM module, a safety filter computes a certified reference that best matches the teleoperation input while satisfying the safety constraint encoded by the CBF. In contrast to existing perception-based safe control frameworks, we directly close the perception-action loop and demonstrate the full capability of safe control in combination with real-time VI-SLAM without any external infrastructure or prior knowledge of the environment. We verify the efficacy of the perceptive safety filter in real-time MAV experiments using exclusively onboard sensing and computation and show that the teleoperated MAV is able to safely navigate through unknown environments despite arbitrary inputs sent by the teleoperator.

翻译：本文针对非专业人员遥操作的微型飞行器（MAV）系统，提出一种结合控制障碍函数（CBF）与视觉惯性同步定位与建图（VI-SLAM）及稠密三维占据地图的感知安全滤波器，以确保在复杂非结构化环境中的安全导航。该系统仅依赖机载惯性测量单元（IMU）测量值、立体红外图像和深度图像，当遥操作输入被判定为不安全时自主进行修正。我们将三维空间中满足以下任一条件的点定义为不安全：（i）被障碍物占据，或（ii）未被建图。在每个时间步，系统通过VI-SLAM融合机载测量值更新环境占据地图，并构建CBF以参数化三维空间中的（不）安全区域。基于CBF与VI-SLAM模块的状态反馈，安全滤波器计算经认证参考轨迹，该轨迹在满足CBF编码的安全约束的同时最佳匹配遥操作输入。与现有基于感知的安全控制框架不同，本文直接闭合感知-动作环路，在不依赖任何外部基础设施或环境先验知识的前提下，完整展示了安全控制与实时VI-SLAM的协同能力。我们通过完全基于机载传感与计算的实时MAV实验验证了感知安全滤波器的有效性，结果表明：尽管操作员发送任意输入，遥操作MAV仍能在未知环境中实现安全导航。