论文标题
增强了在嘈杂环境中定位的归一化互信息
Enhanced Normalized Mutual Information for Localization in Noisy Environments
论文作者
论文摘要
精细的定位是自动驾驶汽车的至关重要任务。尽管文献中已经探讨了这项特定任务的许多算法，但是从商品传感器中获得准确结果的目标仍然是一个挑战。随着自动驾驶汽车从昂贵的原型到生产项目的过渡，对廉价但可靠的解决方案的需求正在迅速增加。本文考虑了使用廉价摄像机捕获图像的场景，并使用预先加载的当地道路上的精细图作为附带信息进行本地化。本文提出的技术通过利用阴影的可能性而不是精确的传感器读数来扩展基于归一化的相互信息的方案，以在嘈杂的环境中进行定位。这种基于统计信号处理的算法增强功能可在性能上获得可观的提高。数值模拟用于突出表示在代表性应用方案中所提出的技术的好处。进行福特图像集的分析以验证这项工作的核心发现。
Fine localization is a crucial task for autonomous vehicles. Although many algorithms have been explored in the literature for this specific task, the goal of getting accurate results from commodity sensors remains a challenge. As autonomous vehicles make the transition from expensive prototypes to production items, the need for inexpensive, yet reliable solutions is increasing rapidly. This article considers scenarios where images are captured with inexpensive cameras and localization takes place using pre-loaded fine maps of local roads as side information. The techniques proposed herein extend schemes based on normalized mutual information by leveraging the likelihood of shades rather than exact sensor readings for localization in noisy environments. This algorithmic enhancement, rooted in statistical signal processing, offers substantial gains in performance. Numerical simulations are used to highlight the benefits of the proposed techniques in representative application scenarios. Analysis of a Ford image set is performed to validate the core findings of this work.
