自主机器人自动采集湿地温室气体，效率远超人工。
WetExplorer: Automating Wetland Greenhouse-Gas Surveys with an Autonomous Mobile Robot
- 用履带式机器人+多传感器融合实现厘米级精准采样定位。
- 户外定位误差仅1.71厘米，视觉模块姿态估计精度达7毫米和3度。
- 无人值守下完成避障与采样，适合长期密集监测需求。
量化湿地温室气体对气候建模和生态修复评估至关重要，但人工采样耗时耗力。本文提出WetExplorer，一种自主履带式机器人，可全自动完成温室气体采样全流程。系统集成低接地压运动、厘米级提升定位、双RTK传感器融合、障碍物避让规划及深度学习感知，运行于容器化ROS2架构。户外测试显示，传感器融合模块平均定位误差为1.71厘米，视觉模块物体位姿估计的平移精度达7毫米、旋转精度达3°；室内测试表明，完整运动规划流程在无须人工干预下，将采样腔体定位在全局70毫米容差内并成功避障。该系统消除人工瓶颈，支持高频、多站点温室气体测量，为饱和湿地地形提供密集、长期数据的可能性。
原文摘要 · Abstract (English)
Quantifying greenhouse-gases (GHG) in wetlands is critical for climate modeling and restoration assessment, yet manual sampling is labor-intensive, and time demanding. We present WetExplorer, an autonomous tracked robot that automates the full GHG-sampling workflow. The robot system integrates low-ground-pressure locomotion, centimeter-accurate lift placement, dual-RTK sensor fusion, obstacle avoidance planning, and deep-learning perception in a containerized ROS2 stack. Outdoor trials verified that the sensor-fusion stack maintains a mean localization error of 1.71 cm, the vision module estimates object pose with 7 mm translational and 3° rotational accuracy, while indoor trials demonstrated that the full motion-planning pipeline positions the sampling chamber within a global tolerance of 70 mm while avoiding obstacles, all without human intervention. By eliminating the manual bottleneck, WetExplorer enables high-frequency, multi-site GHG measurements and opens the door for dense, long-duration datasets in saturated wetland terrain.
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