MatcherCompass: A Deployment-Aware Benchmark to Guide Image Matcher Selection in the Wild

2026-09-22

AI Summary

为了解决野外机器人在不同条件下的图像匹配问题，本文提出了一种名为MatcherCompass的基准测试方法，该方法通过比较多种匹配算法在不同硬件、分辨率和精度下的性能来指导选择合适的匹配器。

Abstract

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/.

Problem

• image correspondences
• field robotics
• deployment-aware benchmark
• local feature matchers
• resource budget

Innovation

• deployment-aware benchmark
• local feature matchers
• runtime and resource constraints