对比三种视觉测量方法在大场景下的精度与稳定性表现
Comparative Study of Vision-Based Metric Measurement for Large-Scale Planar Scenes
- 基于单目相机几何建模，分析俯仰角影响
- 双目法达分米级精度，抗俯仰角变化能力强
- 图像拼接适合小场景，大场景下易失稳
由于远距离感知、镜头变焦和成像条件不稳定，基于视觉的度量距离与面积测量在大规模室外环境中仍具挑战性。本文以真实水库监测场景为背景，利用PTZ摄像头对比三种代表性方法：基于几何的单目测距、基于鸟瞰图变换的图像拼接，以及使用两台协同标定的单目相机的双目测距。针对单目测距，从相机几何出发推导平面定位模型，并分析俯仰角的影响；图像拼接用于大范围地图构建；双目方案实现无需专用立体硬件的远距离测量。实验表明：单目测距在足够大俯仰角下可达米级精度；双目测距可实现分米级精度，且对俯仰角变化不敏感；图像拼接适用于小场景，但随场景增大，稳定性与可扩展性显著下降。
原文摘要 · Abstract (English)
Vision-based metric distance and area measurement remains challenging in large-scale outdoor environments due to long-range sensing, camera zoom, and unstable imaging conditions. This work studies planar metric measurement in a real-world reservoir monitoring scenario using PTZ cameras and compares three representative approaches: geometry-based monocular ranging, image stitching with birds-eye-view transformation, and stereo-based ranging using two jointly calibrated monocular cameras. For monocular ranging, planar localization models are derived from camera geometry and the effect of camera pitch angle is analyzed. Image stitching is investigated for large-area mapping, while a stereo-based scheme is developed for long-range measurement without dedicated stereo hardware. Experiments show clear trade-offs: monocular ranging achieves meter-level accuracy under sufficiently large pitch angles, stereo-based ranging achieves decimeter-level accuracy with reduced sensitivity to pitch variations, and image stitching is effective for small-scale scenes but degrades in stability and scalability as scene size increases.
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