一种面向传感器的现象学框架用于激光雷达退化仿真和SLAM鲁棒性评估
A Sensor-Aware Phenomenological Framework for Lidar Degradation Simulation and SLAM Robustness Evaluation
AI总结：
本文提出一种面向传感器的现象学框架，用于模拟激光雷达退化并评估SLAM系统的鲁棒性，通过保留点云结构并应用多种退化方法，实现可控的仿真测试。
摘要：
基于激光雷达的SLAM系统对恶劣条件如遮挡、噪声和视场（FoV）退化非常敏感，但现有鲁棒性评估方法要么缺乏物理基础，要么无法捕捉传感器特定行为。本文提出了一种面向传感器的现象学框架，可直接在真实点云上模拟可解释的激光雷达退化，实现可控且可重复的SLAM压力测试。与图像衍生的破坏基准（如SemanticKITTI-C）或仅模拟的方法（如lidarsim）不同，所提出的系统在应用结构化dropout、FoV减少、高斯噪声、遮挡掩码、稀疏化和运动扭曲的同时，保留每个点的几何、强度和时间结构。该框架具有自动主题和传感器检测、模块化配置，具有四个严重程度级别（轻-极端），并且实时性能（每帧少于20毫秒）与ROS工作流兼容。在三种激光雷达架构和五种最先进的SLAM系统上进行实验验证，揭示了由传感器设计和环境上下文塑造的不同的鲁棒性模式。开源实现为在物理有意义的退化场景下对基于激光雷达的SLAM进行基准测试提供了实用基础。
英文摘要：
Lidar-based SLAM systems are highly sensitive to adverse conditions such as occlusion, noise, and field-of-view (FoV) degradation, yet existing robustness evaluation methods either lack physical grounding or do not capture sensor-specific behavior. This paper presents a sensor-aware, phenomenological framework for simulating interpretable lidar degradations directly on real point clouds, enabling controlled and reproducible SLAM stress testing. Unlike image-derived corruption benchmarks (e.g., SemanticKITTI-C) or simulation-only approaches (e.g., lidarsim), the proposed system preserves per-point geometry, intensity, and temporal structure while applying structured dropout, FoV reduction, Gaussian noise, occlusion masking, sparsification, and motion distortion. The framework features autonomous topic and sensor detection, modular configuration with four severity tiers (light--extreme), and real-time performance (less than 20 ms per frame) compatible with ROS workflows. Experimental validation across three lidar architectures and five state-of-the-art SLAM systems reveals distinct robustness patterns shaped by sensor design and environmental context. The open-source implementation provides a practical foundation for benchmarking lidar-based SLAM under physically meaningful degradation scenarios.
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