基于SLAM的监控四旋翼导航与故障韧性嵌入式视觉系统
SLAM-Based Navigation and Fault Resilience in a Surveillance Quadcopter with Embedded Vision Systems
AI总结：
本文提出监控四旋翼Veg，集成视觉SLAM、级联控制、故障检测与嵌入式视觉，实现GPS无关导航、航点规划及紧急降落，并验证于仿真和实际测试。
摘要：
我们提出了一种自主空中监控平台Veg，该平台被设计为一种容错四旋翼系统，集成了视觉SLAM以实现不依赖GPS的导航、用于动态稳定性的先进控制架构，以及用于实时物体和人脸识别的嵌入式视觉模块。该平台采用级联控制设计，具有LQR内环和PD外环轨迹控制。它利用ORB-SLAM3进行6自由度定位和闭环检测，并支持通过基于SLAM地图的Dijkstra路径规划进行航点导航。实时故障检测与识别（FDI）系统可检测旋翼故障，并通过重新规划路径执行紧急降落。基于轻量级CNN和PCA的嵌入式视觉系统可实现高精度的机载物体检测和人脸识别。该无人机完全机载运行，使用Raspberry Pi 4和Arduino Nano，并通过仿真和实际测试验证。这项工作将实时定位、故障恢复和嵌入式AI整合到一个适用于受限环境的单一平台上。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
