利用循环网络进行热成像细化的单目ORB-SLAM3深度估计
Thermal Image Refinement with Depth Estimation using Recurrent Networks for Monocular ORB-SLAM3
AI总结：
本文提出利用轻量级监督网络和循环块进行热成像细化，以提升无人机在低光环境下的实时深度估计和SLAM性能，实验表明其在非辐射热数据集上具有更高的精度和鲁棒性。
摘要：
在GPS拒止和视觉退化的环境中，无人飞行器（UAV）的自主导航仍面临挑战。为此，我们研究了在UAV平台上使用单目热成像相机作为独立传感器进行实时深度估计和同时定位与建图（SLAM）。为从热图像中提取深度信息，我们提出了一种新的管道，采用轻量级监督网络，其中包含循环块（RBs）以捕捉时间依赖性，从而实现更稳健的预测。该网络结合了轻量级卷积主干网络和热成像细化网络（T-RefNet）以细化原始热输入并增强特征可见性。经过细化的热图像和预测的深度图被整合到ORB-SLAM3中，实现仅热成像的定位。与以往方法不同，该网络是在定制的非辐射热数据集上训练的，从而避免了高成本的辐射热相机的需求。在数据集和UAV飞行实验中，结果表明在低光条件下具有竞争性的深度精度和鲁棒的SLAM性能。在辐射热VIVID++（室内暗）数据集上，我们的方法实现了约0.06的绝对相对误差，相比基线超过0.11。在我们的非辐射热室内数据集中，基线误差仍高于0.24，而我们的方法则低于0.10。仅热成像的ORB-SLAM3保持平均轨迹误差低于0.4米。
英文摘要：
Autonomous navigation in GPS-denied and visually degraded environments remains challenging for unmanned aerial vehicles (UAVs). To this end, we investigate the use of a monocular thermal camera as a standalone sensor on a UAV platform for real-time depth estimation and simultaneous localization and mapping (SLAM). To extract depth information from thermal images, we propose a novel pipeline employing a lightweight supervised network with recurrent blocks (RBs) integrated to capture temporal dependencies, enabling more robust predictions. The network combines lightweight convolutional backbones with a thermal refinement network (T-RefNet) to refine raw thermal inputs and enhance feature visibility. The refined thermal images and predicted depth maps are integrated into ORB-SLAM3, enabling thermal-only localization. Unlike previous methods, the network is trained on a custom non-radiometric dataset, obviating the need for high-cost radiometric thermal cameras. Experimental results on datasets and UAV flights demonstrate competitive depth accuracy and robust SLAM performance under low-light conditions. On the radiometric VIVID++ (indoor-dark) dataset, our method achieves an absolute relative error of approximately 0.06, compared to baselines exceeding 0.11. In our non-radiometric indoor set, baseline errors remain above 0.24, whereas our approach remains below 0.10. Thermal-only ORB-SLAM3 maintains a mean trajectory error under 0.4 m.
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。
