SLAM-Based Navigation and Fault Resilience in a Surveillance Quadcopter with Embedded Vision Systems
This work provides a fault-tolerant, fully onboard aerial surveillance solution for constrained environments, though it is incremental as it combines existing methods like ORB-SLAM3 and lightweight CNNs into a single platform.
The authors developed an autonomous surveillance quadcopter that integrates visual SLAM for GPS-free navigation, fault detection for rotor failures, and embedded vision for object and face recognition, achieving real-time operation with onboard hardware validated in simulations and real-world tests.
We present an autonomous aerial surveillance platform, Veg, designed as a fault-tolerant quadcopter system that integrates visual SLAM for GPS-independent navigation, advanced control architecture for dynamic stability, and embedded vision modules for real-time object and face recognition. The platform features a cascaded control design with an LQR inner-loop and PD outer-loop trajectory control. It leverages ORB-SLAM3 for 6-DoF localization and loop closure, and supports waypoint-based navigation through Dijkstra path planning over SLAM-derived maps. A real-time Failure Detection and Identification (FDI) system detects rotor faults and executes emergency landing through re-routing. The embedded vision system, based on a lightweight CNN and PCA, enables onboard object detection and face recognition with high precision. The drone operates fully onboard using a Raspberry Pi 4 and Arduino Nano, validated through simulations and real-world testing. This work consolidates real-time localization, fault recovery, and embedded AI on a single platform suitable for constrained environments.