A Sensor-Aware Phenomenological Framework for Lidar Degradation Simulation and SLAM Robustness Evaluation

2025-12-09

AI Summary

LiDAR SLAM systems exhibit insufficient robustness under real-world conditions such as occlusion, sensor noise, and field-of-view (FoV) degradation; existing evaluation methodologies lack physical grounding or sensor-specific fidelity. Method: We propose a phenomenological degradation modeling framework grounded in perceptual hardware characteristics, enabling controllable synthesis of multi-type degradations—e.g., structured packet loss, FoV cropping, and motion distortion—on real point clouds while preserving geometric, intensity, and temporal structural fidelity. The framework supports automatic sensor identification, modular configuration, and real-time ROS integration, and is compatible with diverse LiDARs and SLAM systems. Contribution/Results: Extensive experiments across three LiDAR models and five state-of-the-art SLAM algorithms quantitatively reveal the impact of sensor design and environmental factors on robustness. Our open-source implementation establishes the first physically plausible, reproducible benchmarking platform for LiDAR-SLAM evaluation.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Computer Vision: Adversarial Attacks & Robustness
• Natural Language Processing: Safety and Robustness

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

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.

Problem

• Simulates realistic lidar degradations on real point clouds
• Evaluates SLAM robustness under controlled sensor-aware conditions
• Provides reproducible stress testing for lidar-based SLAM systems

Innovation

• Simulates lidar degradations on real point clouds
• Preserves geometry, intensity, and temporal structure
• Enables real-time, modular SLAM robustness testing