  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.
