构建可解释的激光雷达退化模拟框架，支持真实点云上的可控鲁棒性测试。
A Sensor-Aware Phenomenological Framework for Lidar Degradation Simulation and SLAM Robustness Evaluation
- 基于真实点云，按物理规律模拟遮挡、噪声、视场缩减等退化
- 实测显示不同激光雷达在极端退化下性能差异显著，受设计与环境影响
- 开源工具支持实时测试，适合自动驾驶系统可靠性评估
基于激光雷达的SLAM系统对遮挡、噪声和视场（FoV）退化等恶劣条件高度敏感，但现有鲁棒性评估方法或缺乏物理基础，或无法捕捉传感器特异性行为。本文提出一种传感器感知的、现象学的框架，直接在真实点云上模拟可解释的激光雷达退化，实现可控且可复现的SLAM压力测试。该系统保留每个点的几何、强度和时间结构，同时施加结构化丢弃、视场缩减、高斯噪声、遮挡掩码、稀疏化和运动失真。框架具备自动主题与传感器识别、四档严重度（轻度至极端）模块化配置，单帧处理时间低于20毫秒，兼容ROS工作流。在三种激光雷达架构和五种先进SLAM系统上的实验验证揭示了由传感器设计和环境背景塑造的独特鲁棒性模式。开源实现为在物理意义明确的退化场景下基准测试激光雷达SLAM提供了实用基础。
原文摘要 · Abstract (English)
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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