使用仅摄像头视觉里程计的自主地面车辆自愈视觉恢复
Self-Healing Visual Recovery for Autonomous Ground Vehicles Using Camera-Only Visual Odometry
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中文总结 AI 辅助
研究低成本无人地面车辆仅靠摄像头视觉导航时标线丢失的恢复问题，提出轻量级两阶段恢复方法，结合多种视觉工具，在模拟课程中评估，该方法成功率高，证明仅靠摄像头在成本和计算限制内实现可靠视觉恢复可行。
中文摘要 AI 辅助
低成本无人地面车辆常用于仓库、检查走廊和农田行等室内场所，依靠地面标线导航。标线跟踪虽只需一个摄像头和少量计算能力，但标线受阻或急转弯消失时会失效。本文提出一种轻量级两阶段恢复方法，无需激光雷达、GPS或GPU。线丢失时，机器人先原地旋转，放松颜色检查并跨多帧等待确认；若仍未找到，单目视觉里程计将机器人移回保存的标记位置再尝试。系统使用深度门控HSV线跟踪器、YOLOv8n障碍物检测器和视觉里程计标记映射器，在仅CPU硬件上以20Hz运行。在三个Webots模拟课程的119次故障注入情节中评估该方法，成功率86.6%，中位恢复时间3.26秒。结果表明在实际成本和计算限制内，仅摄像头的无人地面车辆实现可靠视觉恢复是可行的。
英文摘要
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.
发表机构
- Simula Research Laboratory（Simula研究实验室）
- University of Oslo（奥斯陆大学）
机构由 AI 辅助整理，请以论文原文为准。
