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

• URL: http://arxiv.org/abs/2504.15305v2
• Date: Wed, 23 Apr 2025 03:17:04 GMT
• Title: SLAM-Based Navigation and Fault Resilience in a Surveillance Quadcopter with Embedded Vision Systems
• Authors: Abhishek Tyagi, Charu Gaur,
• Abstract summary: We present an autonomous aerial surveillance platform, Veg, designed as a fault-tolerant quadcopter system.<n>It integrates visual SLAM for GPS-independent navigation, advanced control architecture for dynamic stability, and embedded vision modules for real-time object and face recognition.
• Score: 0.6138671548064356
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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