论文标题
更好地在一起：在线概率集团基于3D地标的地图中的检测
Better Together: Online Probabilistic Clique Change Detection in 3D Landmark-Based Maps
论文作者
论文摘要
许多现代的同时本地化和映射（SLAM）技术由于其实时性能而取决于稀疏的地标地图。但是，这些技术经常断言这些地标是随着时间的推移而定位的，称为静态世界。在大多数实际环境中，这很少是这种情况。更糟糕的是，在长期部署的情况下，机器人必定会观察到传统上静态地标的变化，例如，当自动驾驶汽车遇到建筑区时。这项工作解决了这一挑战，该挑战是通过创建一个概率过滤器，该过滤器在产生地标的功能上创建了复杂的三维环境的变化。为此，地标聚集到集团中，并开发了一个过滤器，以在集团中对地标的观察中共同估算它们的持久性。该过滤器使用几何学对象的估计空间阶段先验，从而使动态和半静态对象可以从正式的静态映射中删除。提出的算法在3D模拟环境中进行了验证。
Many modern simultaneous localization and mapping (SLAM) techniques rely on sparse landmark-based maps due to their real-time performance. However, these techniques frequently assert that these landmarks are fixed in position over time, known as the static-world assumption. This is rarely, if ever, the case in most real-world environments. Even worse, over long deployments, robots are bound to observe traditionally static landmarks change, for example when an autonomous vehicle encounters a construction zone. This work addresses this challenge, accounting for changes in complex three-dimensional environments with the creation of a probabilistic filter that operates on the features that give rise to landmarks. To accomplish this, landmarks are clustered into cliques and a filter is developed to estimate their persistence jointly among observations of the landmarks in a clique. This filter uses estimated spatial-temporal priors of geometric objects, allowing for dynamic and semi-static objects to be removed from a formally static map. The proposed algorithm is validated in a 3D simulated environment.
