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: https://github.com/neufieldrobotics/IRSLAM_Baseline

翻译：热红外图像以及其他低对比度视觉退化环境（如水下、冰雪覆盖区域）下的视觉SLAM问题，对许多先进（SOTA）算法而言仍具挑战性。除前端数据关联困难外，热红外图像在长期重定位与地图复用中还存在额外难题：物体的相对温度在昼夜之间会发生剧烈变化。SLAM中常用于重定位的特征描述符无法在这些昼夜变化中保持一致性。我们证明，在现有基于词袋的定位方案中采用学习型特征描述符，能够显著提升热红外图像跨大时间间隔的地点识别能力。为展示训练词库的有效性，我们开发了一套基线SLAM系统，将学习特征及其匹配方法集成至经典SLAM算法中。该系统在具有挑战性的热红外图像上展现了良好的局部跟踪性能，并能克服昼夜热外观剧烈变化实现重定位。我们的代码与数据集已开源：https://github.com/neufieldrobotics/IRSLAM_Baseline