Abstract
Visual SLAM with thermal imagery, and other low contrast visually degraded environments such as underwater, or in areas dominated by snow and ice, remain a difficult problem for many state of the art (SOTA) algorithms. In addition to challenging front-end data association, thermal imagery presents an additional difficulty for long term relocalization and map reuse. The relative temperatures of objects in thermal imagery change dramatically from day to night. Feature descriptors typically used for relocalization in SLAM are unable to maintain consistency over these diurnal changes. We show that learned feature descriptors can be used within existing Bag of Word based localization schemes to dramatically improve place recognition across large temporal gaps in thermal imagery. In order to demonstrate the effectiveness of our trained vocabulary, we have developed a baseline SLAM system, integrating learned features and matching into a classical SLAM algorithm. Our system demonstrates good local tracking on challenging thermal imagery, and relocalization that overcomes dramatic day to night thermal appearance changes. Our code and datasets are available here: this https URL
Abstract (translated)
视觉SLAM与热成像以及其他低对比视觉降噪环境（如水下或被雪和冰主导的区域） remains a difficult problem for many state-of-the-art (SOTA) algorithms. 除了具有挑战性的前端数据关联之外，热成像还提出了长期重定位和地图重用的问题。热成像中物体的相对温度从白天到黑夜急剧变化。用于SLAM中进行重定位的特征描述符通常无法维持这些日间变化的一致性。我们证明了学习到的特征描述符可以用于现有的基于Bag of Word的定位方案，以显著改善大时间间隔热成像中place recognition。为了证明我们训练词汇的有效性，我们开发了一个基于学习特征和匹配的经典SLAM系统。我们的系统在具有挑战性的热成像上表现出良好的局部跟踪能力，并克服了白天到黑夜热成像显著变化的 relocalization。我们的代码和数据集都可以在这里找到：这个链接
URL
https://arxiv.org/abs/2403.19885
