CM-LIUW-Odometry：面向极端退化煤矿巷道的鲁棒高精度LiDAR-惯性-UWB-轮式里程计
CM-LIUW-Odometry: Robust and High-Precision LiDAR-Inertial-UWB-Wheel Odometry for Extreme Degradation Coal Mine Tunnels
AI总结：
针对煤矿巷道无GPS且环境退化导致SLAM困难的问题，提出基于IESKF的CM-LIUW-Odometry多模态紧耦合框架，融合LiDAR、IMU、UWB与轮式里程计并采用自适应运动模式切换，实现了高精度鲁棒定位。
摘要：
在大规模、复杂且无GPS的地下煤矿环境中，同步定位与建图（SLAM）面临重大挑战。传感器必须应对异常工况：GPS不可用阻碍了场景重建和绝对地理参考，不平坦或湿滑的地形降低了轮式里程计的精度，而狭长、特征贫乏的巷道削弱了LiDAR的有效性。为了解决这些问题，我们提出了CoalMine-LiDAR-IMU-UWB-Wheel-Odometry（CM-LIUW-Odometry），这是一种基于迭代误差状态卡尔曼滤波器（IESKF）的多模态SLAM框架。首先，将LiDAR-惯性里程计与UWB绝对定位约束紧密融合，以使SLAM系统与全局坐标对齐。其次，通过紧耦合方式集成轮式里程计，并辅以非完整约束（NHC）和车辆杠杆臂补偿，以解决UWB测量范围之外区域的性能退化问题。最后，自适应运动模式切换机制根据UWB测量范围和环境退化水平动态调整机器人的运动模式。实验结果验证了该方法在真实地下煤矿场景中实现了卓越的精度和鲁棒性，优于现有最先进的方法。我们在Github上开源了这项工作的代码以造福机器人社区。
英文摘要：
Simultaneous Localization and Mapping (SLAM) in large-scale, complex, and GPS-denied underground coal mine environments presents significant challenges. Sensors must contend with abnormal operating conditions: GPS unavailability impedes scene reconstruction and absolute geographic referencing, uneven or slippery terrain degrades wheel odometer accuracy, and long, feature-poor tunnels reduce LiDAR effectiveness. To address these issues, we propose CoalMine-LiDAR-IMU-UWB-Wheel-Odometry (CM-LIUW-Odometry), a multimodal SLAM framework based on the Iterated Error-State Kalman Filter (IESKF). First, LiDAR-inertial odometry is tightly fused with UWB absolute positioning constraints to align the SLAM system with a global coordinate. Next, wheel odometer is integrated through tight coupling, enhanced by nonholonomic constraints (NHC) and vehicle lever arm compensation, to address performance degradation in areas beyond UWB measurement range. Finally, an adaptive motion mode switching mechanism dynamically adjusts the robot's motion mode based on UWB measurement range and environmental degradation levels. Experimental results validate that our method achieves superior accuracy and robustness in real-world underground coal mine scenarios, outperforming state-of-the-art approaches. We open source our code of this work on Github to benefit the robotics community.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
