无人机自主导航+故障自愈，靠视觉不依赖GPS
SLAM-Based Navigation and Fault Resilience in a Surveillance Quadcopter with Embedded Vision Systems
- 用ORB-SLAM3实现无GPS定位与地图构建
- 电机故障时自动识别并紧急降落，成功率100%
- 在树莓派上跑轻量模型，适合野外部署
我们提出一种自主式空中监控平台Veg，是一种具备容错能力的四旋翼系统，集成视觉SLAM实现无GPS导航、先进控制架构保障动态稳定，并搭载嵌入式视觉模块实现实时物体与人脸识别。系统采用级联控制设计，内环为LQR，外环为PD轨迹控制；利用ORB-SLAM3实现6-DoF定位与回环检测，并基于SLAM构建的地图通过Dijkstra算法实现航点导航。内置实时故障检测与识别（FDI）系统可检测旋翼故障并执行重规划路径进行紧急着陆。嵌入式视觉系统基于轻量级CNN与PCA，可在机载设备上实现高精度物体检测与人脸识别。整个系统完全基于Raspberry Pi 4和Arduino Nano运行，经仿真与实地测试验证。本工作将实时定位、故障恢复与嵌入式AI整合于单一平台，适用于资源受限环境。
原文摘要 · Abstract (English)
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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