SLAM-Based Navigation and Fault Resilience in a Surveillance Quadcopter with Embedded Vision Systems
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.
Cite
@article{arxiv.2504.15305,
title = {SLAM-Based Navigation and Fault Resilience in a Surveillance Quadcopter with Embedded Vision Systems},
author = {Abhishek Tyagi and Charu Gaur},
journal= {arXiv preprint arXiv:2504.15305},
year = {2025}
}
Comments
18 pages, 21 figures, 15 tables. Onboard processing using Raspberry Pi 4 and Arduino Nano. Includes ORB-SLAM3-based navigation, LQR control, rotor fault recovery, object detection, and PCA face recognition. Real-world and simulation tests included. Designed for GPS-denied autonomous UAV surveillance