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

Doumegna Mawuto Koudjo Felix, Xianjia Yu, Zhuo Zou, Tomi Westerlund

TL;DR

The paper tackles the lack of physically grounded, sensor-specific benchmarks for evaluating lidar-based SLAM under adverse conditions. It introduces a sensor-aware phenomenological degradation framework that applies interpretable perturbations directly to real lidar point clouds with real-time performance and autonomous sensor configuration. The authors implement modules for dropout, FoV reduction, noise, occlusion, and motion distortion across four severity tiers, validating across three lidars and five SLAM systems to reveal sensor-dependent robustness patterns. The open-source toolkit provides a practical, reproducible platform for cross-sensor SLAM robustness benchmarking and degradation-aware system design.

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.

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

TL;DR

The paper tackles the lack of physically grounded, sensor-specific benchmarks for evaluating lidar-based SLAM under adverse conditions. It introduces a sensor-aware phenomenological degradation framework that applies interpretable perturbations directly to real lidar point clouds with real-time performance and autonomous sensor configuration. The authors implement modules for dropout, FoV reduction, noise, occlusion, and motion distortion across four severity tiers, validating across three lidars and five SLAM systems to reveal sensor-dependent robustness patterns. The open-source toolkit provides a practical, reproducible platform for cross-sensor SLAM robustness benchmarking and degradation-aware system design.

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.

Paper Structure

This paper contains 17 sections, 4 figures, 1 table.

Table of Contents

  1. Introduction
  2. Methodology
  3. Framework Overview
  4. Autonomous Sensor Configuration
  5. Scenario Design and Parameterization
  6. Augmentation (Simulation) Modules
  7. Integration and Reproducibility
  8. Implementation and Experimental Setup
  9. Experimental Results
  10. Quantitative SLAM Evaluation
  11. Discussion
  12. Conclusion
  13. Technical Implementation Details
  14. Configuration and Severity Tiers
  15. Integration and Reproducibility
  16. ...and 2 more sections

Figures (4)

  • Figure 1: Architecture of the proposed adverse environment simulation framework for robustness evaluation of SLAM algorithms.
  • Figure 2: Distribution of Absolute Pose Error (APE) across degradation severities (light--extreme) for three lidar sensors (Avia, Mid360, Ouster) in both indoor and outdoor settings. Each box aggregates APE values across multiple SLAM back-ends, revealing cross-sensor robustness trends and sensitivity to degradation strength.
  • Figure 3: Real-time augmentation dashboard for the OutdoorRoad dataset under the heavy scenario, illustrating per-sensor point counts, reduction ratios, and processing-time comparisons. Each lidar (Ouster, Livox Avia, Livox Mid-360) is automatically detected and processed concurrently through the autonomous configuration module.
  • Figure 4: Bird’s-eye-view (BEV) visualization of multi-lidar frames from the OutdoorRoad dataset under the heavy degradation scenario. The framework preserves cross-sensor alignment and structural geometry while introducing realistic dropout and occlusion consistent with outdoor environments.