发表机构
DGIST（大邱庆北科学技术院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对现场机器人图像匹配器选择缺乏部署感知指导的问题，提出MatcherCompass基准，比较九种匹配流水线在多种分辨率、精度和GPU平台上的精度与资源消耗，并提供选择指南。
AI 中文摘要
在不同时段和传感模态下运行的现场机器人需要在机载时间和资源预算内获得准确的图像对应关系。然而，在单一设备上针对个别方法报告的准确性和运行时间，对于在目标平台上选择匹配器及其配置所提供的指导有限。我们提出了MatcherCompass，一种用于现场机器人中选择局部特征匹配器的部署感知基准。在常见的输入和姿态评估流程下，我们比较了九种经典和学习的匹配流水线，涵盖四种图像分辨率和支持的数值精度。四种视觉条件包括视点变化、可见光和热成像中的昼夜匹配，以及白天可见光-热成像匹配。我们使用误差-召回曲线下面积（AUC）在5°、10°和20°处评估姿态精度，并在四个涵盖工作站和机载计算机的GPU平台上测量每对图像的运行时间、GPU内存和能耗。结果表明，硬件、输入分辨率和数值精度的变化可以将匹配器移过运行时间预算边界，从而改变可行的选择。我们将测量结果组织成一个选择指南，该指南返回满足用户指定时间和资源限制的所有配置，以及它们在所选视觉条件下的精度。MatcherCompass为选择适合机器人传感条件和计算硬件的匹配流水线提供了实测证据。项目页面：此https URL。
英文摘要
Field robots operating across time of day and sensing modalities require accurate image correspondences within onboard time and resource budgets. However, accuracy and runtime reported for individual methods on a single device provide limited guidance for choosing a matcher and its configuration on a target platform. We present MatcherCompass, a deployment-aware benchmark for choosing local feature matchers in field robotics. Under common input and pose-evaluation procedures, we compare nine classical and learned matching pipelines across four image resolutions and supported numerical precisions. Four visual conditions cover viewpoint variation, day--night matching in visible and thermal imagery, and daytime visible--thermal matching. We evaluate pose accuracy using the area under the error--recall curve (AUC) at $5^\circ$, $10^\circ$, and $20^\circ$, and measure runtime, GPU memory, and energy per image pair on four GPU platforms spanning workstation and onboard computers. The results show that changes in hardware, input resolution, and numerical precision can move a matcher across a runtime budget boundary, altering the feasible choices. We organize the measurements into a selection guide that returns all configurations satisfying user-specified time and resource limits, together with their accuracy under the selected visual condition. MatcherCompass provides measured evidence for choosing matching pipelines that fit a robot's sensing conditions and computing hardware. Project page: https://matchercompass.github.io/.
Comments8 pages, 7 figures
