论文标题
3DEG：在地下环境中全局重新定位的数据驱动描述符提取器提取
3DEG: Data-Driven Descriptor Extraction for Global re-localization in subterranean environments
论文作者
论文摘要
当前的全局重新定位算法是基于本地化和映射方法构建的，并笑着依靠扫描匹配和直接点云特征提取，因此是易受伤害的无侵蚀性无苛刻环境，例如洞穴和隧道。在本文中，我们提出了一个新型的全球重新定位框架：a）不需要像大多数方法一样初始猜测，而b）它具有可供选择的顶级辅助剂，可供选择，最后但并非最不重要的一点，但并非最不重要的一点提供基于迅速的重新定位触发模块，以及c）支持完全自动群体的完全自动蛋白质群体。侧重于具有低功能的地下环境，我们选择基于3D激光扫描的范围图像的二手记录器，以维持环境的深度信息。在我们的新方法中，我们利用了最先进的数据驱动的描述性攻击框架来进行位置识别和定向回归，并通过添加一个接线检测模块来增强它，该模块还将描述符用于分类目的。
Current global re-localization algorithms are built on top of localization and mapping methods andheavily rely on scan matching and direct point cloud feature extraction and therefore are vulnerable infeatureless demanding environments like caves and tunnels. In this article, we propose a novel globalre-localization framework that: a) does not require an initial guess, like most methods do, while b)it has the capability to offer the top-kcandidates to choose from and last but not least provides anevent-based re-localization trigger module for enabling, and c) supporting completely autonomousrobotic missions. With the focus on subterranean environments with low features, we opt to usedescriptors based on range images from 3D LiDAR scans in order to maintain the depth informationof the environment. In our novel approach, we make use of a state-of-the-art data-driven descriptorextraction framework for place recognition and orientation regression and enhance it with the additionof a junction detection module that also utilizes the descriptors for classification purposes.
