Thermal-LiDAR Fusion for Robust Tunnel Localization in GNSS-Denied and Low-Visibility Conditions (2505.03565v1)

Abstract: 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.

Thermal-LiDAR Fusion for Tunnel Localization in GNSS-Denied Conditions

The paper "Thermal-LiDAR Fusion for Robust Tunnel Localization in GNSS-Denied and Low-Visibility Conditions" addresses a significant technological challenge in autonomous navigation: reliable localization in environments where traditional techniques fail, such as tunnels, urban disaster zones, and underground structures. The authors propose a novel sensor fusion framework that integrates thermal imaging and LiDAR data to enhance localization robustness in GNSS-denied and perceptually degraded settings.

Overview of the Proposed Framework

The proposed approach leverages the complementary strengths of thermal cameras and LiDAR sensors. LiDAR provides accurate depth perception and structural awareness, which is crucial in environments with complex geometries. Meanwhile, thermal imaging offers resilience against low-light or smoky conditions, characteristics that degrade conventional vision-based systems. The integration is achieved through a loosely coupled framework using an Extended Kalman Filter (EKF), effectively managing multi-sensor inputs with varying sampling rates and occasional sensor outages.

The use of visual odometry and SLAM techniques enables robust motion estimation even in environment types where GNSS signals are not available and traditional localization systems struggle due to lack of distinguishable features.

Numerical Validation and Results

To assess the effectiveness of the proposed sensor fusion framework, tests were conducted in tunnel environments to simulate real-world conditions of sensor degradation and visibility challenges. The results indicate sustained localization accuracy even in conditions where standard approaches fail due to a tunnel's featureless geometry. Notably, the GenZ-ICP was employed for LiDAR odometry while LDSO was applied to thermal imaging, ensuring both point and frame data were optimally utilized.

The quantitative results from these experiments demonstrate that the proposed fusion system significantly mitigates the drift issues associated with standalone odometry systems and provides reliable localization performance.

Implications and Speculation on Future Developments

The implications of this research span both practical and theoretical domains. Practically, the enhancement of autonomous system reliability in hazardous environments unlocks numerous applications, including disaster response robotics and autonomous vehicles in mining and construction fields. Theoretically, this framework adds to the growing body of knowledge on sensor fusion methods, pushing the boundaries of localization technology by exploring thermal and LiDAR integration.

Looking forward, future developments in AI could further improve the sophistication and adaptability of such systems. Improvements in machine learning algorithms may enhance the ability to dynamically calibrate and optimize sensor fusion parameters in real-time, offering even greater accuracy and adaptability to changing environments. The exploration of additional sensors, such as advanced inertial measurement units or radar, could complement thermal and LiDAR sensors, providing even richer data for fusion processes.

In summary, the blending of thermal and LiDAR data offers a promising solution for reliable localization in challenging environments, paving the way for more resilient autonomous systems. The ongoing evolution in sensor technologies and fusion methodologies will undoubtedly continue to enrich the capabilities and applications of autonomous navigation systems.