  Navigating toy drones through uncharted GPS-denied indoor spaces poses
significant difficulties due to their reliance on GPS for location
determination. In such circumstances, the necessity for achieving proper
navigation is a primary concern. In response to this formidable challenge, we
introduce a real-time autonomous indoor exploration system tailored for drones
equipped with a monocular \emph{RGB} camera.
  Our system utilizes \emph{ORB-SLAM3}, a state-of-the-art vision feature-based
SLAM, to handle both the localization of toy drones and the mapping of unmapped
indoor terrains. Aside from the practicability of \emph{ORB-SLAM3}, the
generated maps are represented as sparse point clouds, making them prone to the
presence of outlier data. To address this challenge, we propose an outlier
removal algorithm with provable guarantees. Furthermore, our system
incorporates a novel exit detection algorithm, ensuring continuous exploration
by the toy drone throughout the unfamiliar indoor environment. We also
transform the sparse point to ensure proper path planning using existing path
planners.
  To validate the efficacy and efficiency of our proposed system, we conducted
offline and real-time experiments on the autonomous exploration of indoor
spaces. The results from these endeavors demonstrate the effectiveness of our
methods.
