Rotational Odometry using Ultra Low Resolution Thermal Cameras
This work addresses the need for reliable odometry in robotics under varying lighting conditions, though it is incremental as it adapts existing CNN methods to a new sensor modality.
This study tackles the problem of providing rotational odometry for navigational devices like rovers and drones by using ultra-low-resolution thermal cameras, achieving a method that is robust to lighting conditions and cost-effective, with experiments analyzing the impact of camera resolution and frame count on precision.
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.