SLAM-Based Navigation and Fault Resilience in a Surveillance Quadcopter with Embedded Vision Systems

Fuente: arXiv
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Main Authors: Tyagi, Abhishek, Gaur, Charu
Format: Preprint
Published: 2025
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_version_ 1866912341409398784
author Tyagi, Abhishek
Gaur, Charu
author_facet Tyagi, Abhishek
Gaur, Charu
contents 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.
format Preprint
id arxiv_https___arxiv_org_abs_2504_15305
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle SLAM-Based Navigation and Fault Resilience in a Surveillance Quadcopter with Embedded Vision Systems
Tyagi, Abhishek
Gaur, Charu
Robotics
Computer Vision and Pattern Recognition
Systems and Control
68T40, 68U10, 70Q05
I.2.9; I.4.8; I.2.10; C.3
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.
title SLAM-Based Navigation and Fault Resilience in a Surveillance Quadcopter with Embedded Vision Systems
topic Robotics
Computer Vision and Pattern Recognition
Systems and Control
68T40, 68U10, 70Q05
I.2.9; I.4.8; I.2.10; C.3
url https://arxiv.org/abs/2504.15305