For robots navigating using only a camera, illumination changes in indoor environments can cause localization failures during autonomous navigation. In this paper, we present a multi-session visual SLAM approach to create a map made of multiple variations of the same locations in different illumination conditions. The multi-session map can then be used at any hour of the day for improved localization capability. The approach presented is independent of the visual features used, and this is demonstrated by comparing localization performance between multi-session maps created using the RTAB-Map library with SURF, SIFT, BRIEF, FREAK, BRISK, KAZE, DAISY and SuperPoint visual features. The approach is tested on six mapping and six localization sessions recorded at 30 minutes intervals during sunset using a Google Tango phone in a real apartment.
翻译：对于仅使用相机航行的机器人,室内环境的照明变化可能会在自主导航期间造成本地化失败。在本文中,我们提出了一个多部分直观SLM方法,用于绘制在不同照明条件下同一地点多种变异的地图。然后,多片地图可以在一天的任何时候用于提高本地化能力。所展示的方法与所使用的视觉特征无关,通过将使用RTAB-Map图书馆制作的多片地图与SURF、SIFT、BRIEF、FRECH、BRISK、KAZE、DAISY和SuperPoint视觉特征之间的本地化性能比较,可以证明这一点。该方法在日落期间用谷歌探戈电话在真实公寓中每30分钟记录6个本地化会议,对6个测绘和6个本地化会议进行了测试。
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