提出抗干扰多传感器融合定位框架，提升复杂场景下自动驾驶可靠性
Ultra-Fusion: A Resilient Tightly-Coupled Multi-Sensor Fusion SLAM Framework under Sensor Degradation and Spatiotemporal Perturbation for Intelligent Transportation Systems
- 统一滑动窗口估计器处理异步数据，支持多种传感器组合
- 在长时高速与传感器故障下仍保持高精度定位，可用性显著提升
- 适用于车辆、机器人和无人机，特别适合城市隧道等复杂环境
智能交通系统（如自动驾驶汽车、四足末端配送机器人、基建巡检无人机）的可靠定位至关重要。尽管紧耦合多传感器融合在理想条件下表现优异，但实际部署中仍易受传感器退化（如光照不良、激光雷达失效、车轮打滑、GNSS中断）及时空标定误差影响，这些情况常见于城市峡谷、隧道和高速走廊，导致定位漂移，影响路径跟踪、隧道连续性和局部地图对齐。本文提出Ultra-Fusion，一种基于统一滑动窗口估计器的紧耦合多传感器定位框架。异步测量按时间戳排序并转化为优化窗口内的可选因子，支持WIO、VIO、LIO、LVIO，并可选加入轮速计与GNSS。可观测性感知初始化自动选择启动模式，因子级可靠性调度过滤退化数据，在线激光雷达-惯导时空标定在运行中修正时间偏移与旋转外参。我们扩展了M3DGR基准测试集，添加仿真轨迹，并在M3DGR、M2DGR-Plus、KAIST、GrandTour、MARS-LVIG上评估超过60个开源SLAM系统。结果表明，在长时、高速、退化及标定扰动条件下，该框架在轮式、腿式和空中平台均表现出色，显著提升道路级自主、校园与仓库移动及低空巡检的定位可用性。论文被接受后将公开源代码与数据集。
原文摘要 · Abstract (English)
Reliable localization is essential for intelligent transportation systems (ITS), including autonomous vehicles, quadruped last-mile carriers, and infrastructure-inspection unmanned aerial vehicles (UAVs). Although tightly-coupled multi-sensor fusion improves accuracy in favorable conditions, deployed systems remain vulnerable to sensor degradation -- poor illumination, LiDAR degeneracy, wheel slippage, and GNSS outage -- and to spatiotemporal calibration errors. These failures are common in urban canyons, tunnels, and high-speed corridors, where localization drift can degrade route tracking, tunnel passage continuity, and local map alignment. This paper presents Ultra-Fusion, a tightly-coupled multi-sensor localization framework based on a unified sliding-window estimator. Asynchronous measurements are timestamp-ordered and converted into optional factors within one optimization window, supporting WIO, VIO, LIO, and LVIO with optional wheel and GNSS augmentation. Observability-aware initialization selects the bootstrap mode, factor-wise reliability scheduling gates degraded measurements, and online LiDAR--IMU spatiotemporal calibration refines temporal offsets and rotational extrinsics during operation. We extend the M3DGR benchmark with simulation trajectories and evaluate more than 60 open-source SLAM systems on M3DGR, M2DGR-Plus, KAIST, GrandTour, and MARS-LVIG. The results show competitive accuracy across wheeled, legged, and aerial platforms under long-duration and high-speed operation, degradation, and calibration perturbation, improving localization availability for road-level autonomy, campus and warehouse mobility, and low-altitude aerial inspection. To benefit the industrial and academic community, we will release source code and datasets upon paper acceptance.
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