面向矿用服务车辆的、与抗失锁GNSS融合紧耦合的激光雷达-惯性SLAM
A LiDAR-Inertial SLAM Tightly-Coupled with Dropout-Tolerant GNSS Fusion for Autonomous Mine Service Vehicles
- GNSS Research Center, Wuhan University（武汉大学GNSS研究中心）
- CHC NAVIGATION（华测导航）
- Beijing Lishedachuan Co., Ltd（北京立世达川科技有限公司）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对矿山场景卫星信号失锁、观测退化等导航难题，提出一种结合卡尔曼滤波与图优化的紧耦合多模态激光雷达-惯性SLAM系统，通过多激光雷达协同、面元配准与回环重初始化，在GNSS长期失锁下仍能保持米级导航精度。
AI中文摘要：
多模态传感器融合已成为现实世界导航系统的关键前提，近期研究已在多个领域实现该系统的成功部署。但在矿山场景的导航任务中，受卫星信号失锁、感知质量下降、观测退化等问题影响，导航仍面临挑战。为解决该问题，本文提出一种结合卡尔曼滤波与图优化的激光雷达-惯性里程计方法。前端由多个并行运行的激光雷达-惯性里程计组成，将激光点、IMU、轮式里程计信息在误差状态卡尔曼滤波器中紧融合；我们采用面元进行配准，而非常用的特征点。后端构建位姿图，联合优化来自惯性、激光雷达里程计、全球导航卫星系统（GNSS）的位姿估计结果。由于车辆在隧道内运行时间长，累积的大量漂移可能无法被GNSS测量完全修正，因此我们利用基于回环检测的重初始化过程实现完全对齐。此外，系统通过处理数据丢失、数据流一致性、估计误差提升了鲁棒性。实验结果表明，借助多激光雷达协同与面元配准，系统对长期退化具有良好的耐受性，即使在GNSS失锁状态下运行数十分钟，仍能达到米级精度。
英文摘要：
Multi-modal sensor integration has become a crucial prerequisite for the real-world navigation systems. Recent studies have reported successful deployment of such system in many fields. However, it is still challenging for navigation tasks in mine scenes due to satellite signal dropouts, degraded perception, and observation degeneracy. To solve this problem, we propose a LiDAR-inertial odometry method in this paper, utilizing both Kalman filter and graph optimization. The front-end consists of multiple parallel running LiDAR-inertial odometries, where the laser points, IMU, and wheel odometer information are tightly fused in an error-state Kalman filter. Instead of the commonly used feature points, we employ surface elements for registration. The back-end construct a pose graph and jointly optimize the pose estimation results from inertial, LiDAR odometry, and global navigation satellite system (GNSS). Since the vehicle has a long operation time inside the tunnel, the largely accumulated drift may be not fully by the GNSS measurements. We hereby leverage a loop closure based re-initialization process to achieve full alignment. In addition, the system robustness is improved through handling data loss, stream consistency, and estimation error. The experimental results show that our system has a good tolerance to the long-period degeneracy with the cooperation different LiDARs and surfel registration, achieving meter-level accuracy even for tens of minutes running during GNSS dropouts.
