这篇论文教你怎么用单摄像头和CPU让无人车自己找回丢失的导引线，测试了119次，86%成功，三秒多就恢复。适合做低成本导航的开发者参考。
该论文提出一种两阶段视觉恢复方法，针对仅依赖单摄像头和无GPU的低成本无人车（UGV）在室内导引线丢失时的恢复问题。第一阶段通过原地旋转并逐步放松颜色检测阈值重新找线；第二阶段使用单目视觉里程计将机器人回溯到保存的轨迹点。系统包含深度门控HSV线跟踪器、YOLOv8n障碍物检测器和视觉里程计面包屑映射器，在CPU上以20Hz运行。在Webots模拟的三个赛道共119次故障注入测试中，成功率为86.6%，中位恢复时间3.26秒。
Self-Healing Visual Recovery for Autonomous Ground Vehicles Using Camera-Only Visual Odometry
Low-cost unmanned ground vehicles are often used in indoor places like warehouses, inspection corridors, and farm rows, where painted floor lines guide the robot. Line following is useful because it only needs one camera and little computing power, but it can fail when the line is blocked or turns sharply and goes out of view. Sensor-rich platforms tolerate this through hardware redundancy (LiDAR, GPS, multiple cameras), but camera-only systems must recover at runtime with no additional infrastructure. This paper presents a lightweight, two-stage recovery approach that restores guideline tracking without LiDAR, GPS, or a GPU. When the line is lost, the robot first turns in place while slowly relaxing its color checks and waiting for confirmation across multiple frames (Stage 1). If the line is still not found, monocular visual odometry moves the robot back to saved breadcrumb positions before it tries again (Stage 2). The system uses a depth-gated HSV line tracker, a YOLOv8n obstacle detector, and a visual odometry breadcrumb mapper, and it runs at 20 Hz on CPU-only hardware. The controller embeds a complete MAPE-K loop within a single 50 ms control tick, with no external adaptation manager required. The approach is evaluated across 119 fault-injected episodes on three Webots simulation courses. The method was successful in 86.6% of cases, with a median recovery time of 3.26 seconds. These results demonstrate that reliable visual recovery is feasible on camera-only UGVs within practical cost and computational limits.