迈向鲁棒的多传感器融合地面SLAM：一个综合基准与弹性框架
Towards Robust Sensor-Fusion Ground SLAM: A Comprehensive Benchmark and A Resilient Framework
- Independent（独立研究者）
- Chongqing University（重庆大学）
- Nankai University（南开大学）
- Zhejiang University of Technology（浙江工业大学）
- Shanghai Jiao Tong University（上海交通大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对SLAM在极端情况下的鲁棒性局限及现有基准与融合框架的不足，提出包含多退化模式的M3DGR数据集，评估40个SLAM系统，并开发耦合多传感器的弹性框架Ground-Fusion++。
AI中文摘要：
针对结构化环境定制的SLAM方法已取得显著进展，但其在具有挑战性的极端情况下的鲁棒性仍是一个关键局限。尽管集成多种传感器的多传感器融合方法展现出了可期的性能提升，但研究界仍面临两大关键障碍：一方面，缺乏能在各种退化场景下系统评估SLAM算法的标准化和可配置基准，阻碍了全面的性能评估；另一方面，现有的SLAM框架主要侧重于融合有限的传感器类型，未能有效应对针对不同环境条件的自适应传感器选择策略。为了弥补这些差距，我们做出了三项关键贡献：首先，我们引入了M3DGR数据集：这是一个包含视觉挑战、LiDAR退化、车轮打滑和GNSS拒止等系统诱导退化模式的富传感器基准。其次，我们在M3DGR上对40个SLAM系统进行了全面评估，提供了关于它们在具有挑战性的现实条件下鲁棒性和局限性的关键见解。第三，我们开发了一个名为Ground-Fusion++的弹性模块化多传感器融合框架，该框架通过耦合GNSS、RGB-D、LiDAR、IMU（惯性测量单元）和轮式里程计展现出鲁棒的性能。代码和数据集已公开。
英文摘要：
Considerable advancements have been achieved in SLAM methods tailored for structured environments, yet their robustness under challenging corner cases remains a critical limitation. Although multi-sensor fusion approaches integrating diverse sensors have shown promising performance improvements, the research community faces two key barriers: On one hand, the lack of standardized and configurable benchmarks that systematically evaluate SLAM algorithms under diverse degradation scenarios hinders comprehensive performance assessment. While on the other hand, existing SLAM frameworks primarily focus on fusing a limited set of sensor types, without effectively addressing adaptive sensor selection strategies for varying environmental conditions. To bridge these gaps, we make three key contributions: First, we introduce M3DGR dataset: a sensor-rich benchmark with systematically induced degradation patterns including visual challenge, LiDAR degeneracy, wheel slippage and GNSS denial. Second, we conduct a comprehensive evaluation of forty SLAM systems on M3DGR, providing critical insights into their robustness and limitations under challenging real-world conditions. Third, we develop a resilient modular multi-sensor fusion framework named Ground-Fusion++, which demonstrates robust performance by coupling GNSS, RGB-D, LiDAR, IMU (Inertial Measurement Unit) and wheel odometry. Codes and datasets are publicly available.
