论文标题
基于地面纹理本地化的功能 - 调查
Features for Ground Texture Based Localization -- A Survey
论文作者
论文摘要
使用基于特征的方法的基于地面纹理的车辆定位是一种实现无基础设施的高临界度定位的有前途的方法。在本文中，我们使用单独拍摄的图像对以及合成转换提供了对本任务的可用特征提取方法的首次广泛评估。我们将Akaze，Surf和Censure确定为最佳性能的关键点探测器，并发现与Orb，简短和闩锁特征描述符的谴责配对，以实现最大的成功率来逐步定位，而SIFT在考虑在绝对定位期间可能发生的严重合成转换时会脱颖而出。
Ground texture based vehicle localization using feature-based methods is a promising approach to achieve infrastructure-free high-accuracy localization. In this paper, we provide the first extensive evaluation of available feature extraction methods for this task, using separately taken image pairs as well as synthetic transformations. We identify AKAZE, SURF and CenSurE as best performing keypoint detectors, and find pairings of CenSurE with the ORB, BRIEF and LATCH feature descriptors to achieve greatest success rates for incremental localization, while SIFT stands out when considering severe synthetic transformations as they might occur during absolute localization.
