FIRE-LIVWO：基于抗失效毫米波雷达增强的鲁棒激光雷达-惯性-视觉-轮式里程计
FIRE-LIVWO: Robust LiDAR-Inertial-Visual-Wheel Odometry via Failure-Immune mmWave Radar Enhancement
浏览论文内容
中文总结 AI 辅助
该研究针对地下煤矿SLAM的退化问题，提出基于IESKF的FIRE-LIVWO多模态里程计，通过自适应融合策略提升鲁棒性，实测平均定位误差5.677m，已开源代码。
中文摘要 AI 辅助
在结构复杂、退化严重的大型地下煤矿中实现鲁棒的SLAM（同步定位与建图）仍极具挑战。浓密的烟雾和粉尘会造成视觉信息大量丢失，降低激光雷达点云特征质量；而狭长、自相似的走廊会引发几何退化，导致里程计漂移显著。为解决这些问题，本文提出FIRE-LIVWO：抗失效毫米波雷达增强型激光雷达-惯性-视觉-轮式里程计，这是一种基于迭代误差状态卡尔曼滤波（IESKF）的紧耦合多模态里程计框架。该框架在统一的体素地图（VoxelMap）中融合4D毫米波雷达、激光雷达和视觉特征，联合构建激光雷达-雷达点到平面残差与稀疏视觉光度残差。在充满烟雾的环境中，利用4D毫米波雷达的强穿透性，并引入逐点多普勒速度约束以保持状态可观测性；在几何退化的走廊中，通过非完整约束（NHC）和在线杠杆臂补偿紧耦合轮式里程计，减少漂移。本文的核心贡献是基于几何与视觉可观测性分析的退化检测与自适应融合模型切换策略，该策略可在线量化可观测性并动态调整模态权重。在地下煤矿的真实实验表明，FIRE-LIVWO能准确识别失效边界，在极端条件下实现可靠的模态切换；与基线方法相比，其精度和鲁棒性更优，平均定位误差为5.677m。本文在Github上开源代码，以惠及机器人领域社区。
英文摘要
Achieving robust SLAM in large-scale underground coal mines with complex structures and severe degeneracies remains highly challenging. Dense smoke and dust cause substantial loss of visual information and degrade LiDAR point-cloud features, while long, self-similar corridors induce geometric degeneration, leading to pronounced odometry drift. To address these issues, we propose FIRE-LIVWO: Failure-Immune mmWave Radar-Enhanced LiDAR-Inertial-Visual-Wheel Odometry, a tightly coupled multi-modal odometry framework based on an iterated error-state Kalman filter (IESKF). The framework fuses 4D mmWave radar, LiDAR, and visual features within a unified VoxelMap and jointly constructs LiDAR-radar point-to-plane residuals and sparse visual photometric residuals. In smoke-filled environments, we exploit the strong penetration of 4D mmWave radar and introduce pointwise Doppler velocity constraints to preserve state observability. In geometrically degenerate corridors, we tightly couple wheel odometry using non-holonomic constraints (NHC) and online lever-arm compensation to reduce drift. Our central contribution is a degeneration detection and adaptive fusion model switching strategy grounded in geometric and visual observability analysis, which quantifies observability online and dynamically adjusts modality weights. Real-world experiments in underground coal mines demonstrate that FIRE-LIVWO accurately identifies failure boundaries, enabling reliable modality switching under extreme conditions. Compared with baselines, it achieves superior accuracy and robustness (average localization error of 5.677m). We open source our code on Github to benefit the robotics community.
发表机构
- School of Mechatronic Engineering, China University of Mining and Technology（中国矿业大学机电工程学院）
- Jiangsu Collaborative Innovation Center of Intelligent Mining Equipment, China University of Mining and Technology（中国矿业大学江苏省智能采矿装备协同创新中心）
- National Key Laboratory of Intelligent Mining Equipment Technology, China University of Mining and Technology（中国矿业大学智能采矿装备技术全国重点实验室）
机构由 AI 辅助整理，请以论文原文为准。
