论文标题
使用紧凑型二进制描述符基于地面纹理的本地化
Ground Texture Based Localization Using Compact Binary Descriptors
论文作者
论文摘要
基于地面纹理的本地化是实现车辆高准确定位的一种有前途的方法。我们提出了一种独立的方法，该方法可用于全局本地化以及随后的本地本地化更新，即，它允许机器人本地化，而无需了解其当前的下落，但它也可以利用先前的姿势估算来大大减少计算时间。我们的方法基于一种新颖的匹配策略，我们称之为身份匹配，该策略基于紧凑的二进制特征描述符。身份匹配的零件对特征对匹配仅当它们的描述符相同时。尽管其他用于全局本地化的方法的计算速度更快，但我们的方法达到了更高的定位成功率，并且可以在初始定位后切换到本地定位。
Ground texture based localization is a promising approach to achieve high-accuracy positioning of vehicles. We present a self-contained method that can be used for global localization as well as for subsequent local localization updates, i.e. it allows a robot to localize without any knowledge of its current whereabouts, but it can also take advantage of a prior pose estimate to reduce computation time significantly. Our method is based on a novel matching strategy, which we call identity matching, that is based on compact binary feature descriptors. Identity matching treats pairs of features as matches only if their descriptors are identical. While other methods for global localization are faster to compute, our method reaches higher localization success rates, and can switch to local localization after the initial localization.
