面向GNSS拒止与低能见度条件下稳健隧道定位的热成像-LiDAR融合方法
Thermal-LiDAR Fusion for Robust Tunnel Localization in GNSS-Denied and Low-Visibility Conditions
AI总结：
针对隧道等GNSS拒止、低能见度且特征匮乏环境中的定位难题，本文提出热成像相机与LiDAR融合框架，结合EKF、视觉里程计和SLAM实现稳健定位，并在隧道实验中验证其优于标准方法。
摘要：
尽管自主导航已取得显著进展，但在隧道、城市灾害区域和地下结构等危险环境中确保可靠定位仍存在关键空白。隧道构成一种独特且困难的场景：不仅容易丢失GNSS信号，而且由于墙壁重复、光照条件差，几乎无法为视觉定位提供特征。这些条件会使依赖可区分环境特征的常规视觉和LiDAR系统性能退化。为解决这一问题，我们提出一种新颖的传感器融合框架，将热成像相机与LiDAR集成，以在隧道及其他感知退化环境中实现稳健定位。热成像相机在低光照或烟雾条件下提供韧性，而LiDAR提供精确的深度感知和结构感知能力。通过结合这些传感器，我们的框架能够在多样且动态的环境中确保持续且准确的定位。我们使用扩展卡尔曼滤波器（EKF）融合多传感器输入，并利用视觉里程计和SLAM（同时定位与建图）技术处理传感器数据，从而即使在GNSS拒止环境中也能实现稳健的运动估计和建图。这种传感器模态融合不仅增强了系统韧性，还为网联自动驾驶车辆（CAVs）中的信息物理系统提供了可扩展的解决方案。为验证该框架，我们在隧道环境中进行了测试，模拟传感器退化和能见度挑战。结果表明，在标准方法因隧道无特征几何结构而性能下降的情况下，我们的方法仍能保持准确且持续的定位。该框架的多功能性使其成为自主车辆、巡检机器人以及其他在受限且感知条件恶劣环境中运行的信息物理系统的有前景的解决方案。
英文摘要：
Despite significant progress in autonomous navigation, a critical gap remains in ensuring reliable localization in hazardous environments such as tunnels, urban disaster zones, and underground structures. Tunnels present a uniquely difficult scenario: they are not only prone to GNSS signal loss, but also provide little features for visual localization due to their repetitive walls and poor lighting. These conditions degrade conventional vision-based and LiDAR-based systems, which rely on distinguishable environmental features. To address this, we propose a novel sensor fusion framework that integrates a thermal camera with a LiDAR to enable robust localization in tunnels and other perceptually degraded environments. The thermal camera provides resilience in low-light or smoke conditions, while the LiDAR delivers precise depth perception and structural awareness. By combining these sensors, our framework ensures continuous and accurate localization across diverse and dynamic environments. We use an Extended Kalman Filter (EKF) to fuse multi-sensor inputs, and leverages visual odometry and SLAM (Simultaneous Localization and Mapping) techniques to process the sensor data, enabling robust motion estimation and mapping even in GNSS-denied environments. This fusion of sensor modalities not only enhances system resilience but also provides a scalable solution for cyber-physical systems in connected and autonomous vehicles (CAVs). To validate the framework, we conduct tests in a tunnel environment, simulating sensor degradation and visibility challenges. The results demonstrate that our method sustains accurate localization where standard approaches deteriorate due to the tunnels featureless geometry. The frameworks versatility makes it a promising solution for autonomous vehicles, inspection robots, and other cyber-physical systems operating in constrained, perceptually poor environments.
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。
