SLAM-Based Navigation and Fault Resilience in a Surveillance Quadcopter with Embedded Vision Systems
Journal:
arXiv
Published Date:
Apr 18, 2025
Abstract
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.
