This work introduces a simulator-based benchmark for visual localization in the autonomous navigation context. The dynamic benchmark enables investigation of how variables such as the time of day, weather, and camera perspective affect the navigation performance of autonomous agents that utilize visual localization for closed-loop control. The experimental part of the paper studies the effects of four such variables by evaluating state-of-the-art visual localization methods as part of the motion planning module of an autonomous navigation stack. The results show major variation in the suitability of the different methods for vision-based navigation. To the authors' best knowledge, the proposed benchmark is the first to study modern visual localization methods as part of a complete navigation stack. We make the benchmark available at https://github.com/lasuomela/carla_vloc_benchmark.

翻译：这项工作引入了自主导航背景下视觉定位模拟基准。动态基准有助于调查诸如时间、天气和相机视角等变量如何影响利用视觉定位进行闭路控制的自主代理器的导航性能。该文件的实验部分通过评价作为自主导航堆的动作规划模块的一部分的先进视觉定位方法,对四个此类变量的效果进行了研究。结果显示不同视觉导航方法的适宜性存在重大差异。根据作者的最佳知识,拟议基准是首先研究现代视觉定位方法,作为完整导航堆集的一部分。我们在https://github.com/lasomela/carla_vloc_benchmark上公布了基准。