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.
Abstract (translated)
我们介绍了一种自主空中监控平台Veg，该平台设计为一个容错四旋翼系统，集成了视觉SLAM（即时定位与地图构建）技术以实现无需GPS的导航、先进的控制架构以确保动态稳定性以及嵌入式视觉模块进行实时物体和面部识别。 该平台采用了级联控制系统设计，包括LQR内环和PD外环轨迹控制。它利用ORB-SLAM3进行六自由度（6-DoF）定位和闭环检测，并通过基于SLAM生成的地图的迪杰斯特拉路径规划支持航路点导航。实时故障检测与识别（FDI）系统可以检测到旋翼故障并执行紧急着陆。 嵌入式视觉系统基于轻量级卷积神经网络（CNN）和主成分分析（PCA），能够实现高精度的机载物体检测和面部识别。该无人机完全在机载硬件上运行，包括树莓派4和Arduino Nano，并通过模拟测试和实际世界测试进行了验证。 这项工作将实时定位、故障恢复以及嵌入式AI整合到一个适用于受限环境的单一平台上。
URL
https://arxiv.org/abs/2504.15305
