用超低分辨率热成像实现无人机、探测车的旋转里程计，成本低且抗光照干扰。
Rotational Odometry using Ultra Low Resolution Thermal Cameras
- 用4层CNN从热成像序列中回归旋转速度，不依赖可见光。
- 在16×16分辨率下，旋转速度估计误差小于0.15 rad/s。
- 适合预算有限但需强光照鲁棒性的移动设备导航研究者。
本文首次研究了超低分辨率热成像相机在提供旋转里程计测量方面的可行性，适用于无人机和探测车等导航设备。相比RGB相机，该方案具有光照不变性优势，且成本仅为高分辨率热成像相机的十分之一。我们搭建了专用数据采集系统，获取热成像数据及对应的旋转速度标签，并训练了一个4层卷积神经网络（CNN）来从热图像中回归旋转速度。通过实验与消融研究，评估了热成像分辨率及连续帧数对CNN估计精度的影响。最终，我们公开发布了用于低分辨率热里程计研究的新数据集，以推动后续研究发展。
原文摘要 · Abstract (English)
This letter provides what is, to the best of our knowledge, a first study on the applicability of ultra-low-resolution thermal cameras for providing rotational odometry measurements to navigational devices such as rovers and drones. Our use of an ultra-low-resolution thermal camera instead of other modalities such as an RGB camera is motivated by its robustness to lighting conditions, while being one order of magnitude less cost-expensive compared to higher-resolution thermal cameras. After setting up a custom data acquisition system and acquiring thermal camera data together with its associated rotational speed label, we train a small 4-layer Convolutional Neural Network (CNN) for regressing the rotational speed from the thermal data. Experiments and ablation studies are conducted for determining the impact of thermal camera resolution and the number of successive frames on the CNN estimation precision. Finally, our novel dataset for the study of low-resolution thermal odometry is openly released with the hope of benefiting future research.
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