Despite promising SLAM research in both vision and robotics communities, which fundamentally sustains the autonomy of intelligent unmanned systems, visual challenges still threaten its robust operation severely. Existing SLAM methods usually focus on specific challenges and solve the problem with sophisticated enhancement or multi-modal fusion. However, they are basically limited to particular scenes with a non-quantitative understanding and awareness of challenges, resulting in a significant performance decline with poor generalization and(or) redundant computation with inflexible mechanisms. To push the frontier of visual SLAM, we propose a fully computational reliable evaluation module called CEMS (Challenge Evaluation Module for SLAM) for general visual perception based on a clear definition and systematic analysis. It decomposes various challenges into several common aspects and evaluates degradation with corresponding indicators. Extensive experiments demonstrate our feasibility and outperformance. The proposed module has a high consistency of 88.298% compared with annotation ground truth, and a strong correlation of 0.879 compared with SLAM tracking performance. Moreover, we show the prototype SLAM based on CEMS with better performance and the first comprehensive CET (Challenge Evaluation Table) for common SLAM datasets (EuRoC, KITTI, etc.) with objective and fair evaluations of various challenges. We make it available online to benefit the community on our website.

尽管视觉和机器人领域的 SLAM 研究前景广阔，从根本上维持了智能无人系统的自主性，但视觉挑战仍然严重威胁着其稳健运行。现有的SLAM方法通常专注于特定的挑战，并通过复杂的增强或多模态融合来解决问题。然而，它们基本上仅限于特定场景，对挑战缺乏定量的理解和认识，导致性能显着下降，泛化能力差和（或）冗余计算，机制不灵活。为了推动视觉 SLAM 的前沿发展，我们基于清晰的定义和系统分析，提出了一种完全计算可靠的评估模块，称为 CEMS（SLAM 挑战评估模块），用于一般视觉感知。它将各种挑战分解为几个共同的方面，并用相应的指标来评估退化。大量的实验证明了我们的可行性和优异的性能。所提出的模块与标注groundtruth相比具有88.298%的高一致性，与SLAM跟踪性能相比具有0.879的强相关性。此外，我们还展示了基于CEMS的性能更好的原型SLAM，以及第一个针对常见SLAM数据集（EuRoC、KITTI等）的综合CET（挑战评估表），对各种挑战进行了客观公正的评估。我们将其在线提供，以使我们网站上的社区受益。