用超低分辨率热成像+陀螺仪融合，实现省电高效的旋转位姿估计。
Deep Fusion of Ultra-Low-Resolution Thermal Camera and Gyroscope Data for Lighting-Robust and Compute-Efficient Rotational Odometry
- 热成像与陀螺仪数据融合，抗光照变化
- 分辨率降低后仍保持高精度，计算开销小
- 适合无人机等资源受限设备实时部署
准确的旋转位姿估计对自主机器人系统至关重要，尤其适用于小型、功耗受限平台如无人机和移动机器人。本文提出热成像-陀螺仪融合方法，将超低分辨率热成像与陀螺仪读数结合用于旋转位姿估计。相比可见光相机，热成像不受光照影响；与陀螺仪融合可缓解惯性传感器常见的漂移问题。我们构建了多模态数据采集系统，在多种环境下同步获取热成像与陀螺仪数据，并标注旋转速度。随后设计并训练了一个轻量级卷积神经网络（CNN），融合双模态数据进行旋转速度估计。分析表明，该方法在大幅降低热成像分辨率的情况下仍能保持较高精度，显著提升计算效率与内存利用率。这些优势使其适用于资源受限机器人的实时部署。最后，为促进后续研究，我们公开发布数据集作为补充材料。
原文摘要 · Abstract (English)
Accurate rotational odometry is crucial for autonomous robotic systems, particularly for small, power-constrained platforms such as drones and mobile robots. This study introduces thermal-gyro fusion, a novel sensor fusion approach that integrates ultra-low-resolution thermal imaging with gyroscope readings for rotational odometry. Unlike RGB cameras, thermal imaging is invariant to lighting conditions and, when fused with gyroscopic data, mitigates drift which is a common limitation of inertial sensors. We first develop a multimodal data acquisition system to collect synchronized thermal and gyroscope data, along with rotational speed labels, across diverse environments. Subsequently, we design and train a lightweight Convolutional Neural Network (CNN) that fuses both modalities for rotational speed estimation. Our analysis demonstrates that thermal-gyro fusion enables a significant reduction in thermal camera resolution without significantly compromising accuracy, thereby improving computational efficiency and memory utilization. These advantages make our approach well-suited for real-time deployment in resource-constrained robotic systems. Finally, to facilitate further research, we publicly release our dataset as supplementary material.
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