外观
论文arXiv 2609.186281 个月前
传感器退化、误标定与动态遮挡下地下环境中视觉惯性里程计基准评测
原文链接：Benchmarking Visual-Inertial Odometry in Subterranean Environments Under Sensor Degradation, Miscalibration, and Dynamic Occlusion（arXiv 2609.18628 · 发布日期：2026-09-16）
Benchmarking Visual-Inertial Odometry in Subterranean Environments Under Sensor Degradation, Miscalibration, and Dynamic Occlusion
该工作面向地下环境VIO部署失效，基于CERBERUS数据集构建压力测试基准并评测代表性系统。
核心要点
- 现有评测多关注正常条件精度，难以揭示实际部署中何时发生失效。
- 提出面向失效的VIO压力测试，覆盖IMU退化、相机内外参漂移和动态遮挡。
- 评测四类代表性VIO系统，并用覆盖率与失效阈值分析鲁棒边界。
- 结果显示不同范式脆弱性不同，完整基准脚本和评测流程将发布。
验证范围与局限：验证范围为CERBERUS数据集上的地下环境、四个VIO系统和九种扰动设置。
英文摘要 (Abstract)
Visual-inertial odometry (VIO) is a core capability for autonomous operation in GPS-denied subterranean environments, yet its reliability can degrade sharply under sensor drift, calibration errors, and dynamic occlusion. Existing evaluations mainly emphasize nominal-condition accuracy, offering limited insight into when practical deployment failures occur. In this work, we present a failure-centric stress-test benchmark for VIO in underground environments using the CERBERUS dataset. We systematically evaluate four representative VIO systems spanning filtering-, optimization-, and learning-based paradigms under nine practical perturbation settings, including IMU bias and noise variation, camera intrinsic and extrinsic drift, and dynamic scene occlusion. Beyond conventional trajectory error, we analyze robustness limits through coverage ratio and failure thresholds, revealing breakdown behaviors that are not captured by nominal-condition performance alone. Our study shows distinct vulnerability patterns across VIO paradigms: some methods are more sensitive to inertial degradation, while others are more affected by geometric miscalibration or dynamic interference. These results provide deployment-oriented guidance for VIO selection, calibration prioritization, and reliable operation in challenging underground scenarios. To support reproducible evaluation and future extensions, we will release the full benchmark scripts and evaluation pipeline.
作者：Yueying Zhu, Xiang Li, Thien‐Minh Nguyen, Xuehe Wang, Shenghai Yuan
来源
本页事实对应的公开页面，以原文为准。