This paper presents a state-of-the-art filter that reduces the complexity in object detection, tracking and mapping applications. Existing edge detection and tracking methods are proposed to create suitable autonomy for mobile robots, however many of them face overconfidence and large computations at the entrance to scenarios with an immense number of landmarks. In particular, it is not practically efficient to solely rely on limited sensors such as a camera. The method in this work, the Line-Circle-Square (LCS) filter, claims that mobile robots without a large database for object recognition and highly advanced prediction methods can deal with incoming objects that the camera captures in real-time. The proposed filter applies detection, tracking and learning to each defined expert to extract more information for judging scenes without over-calculation. The interactive learning feed between each expert creates a minimal error that works against overwhelming detected features in crowded scenes. Our experts are dependent on trust factors' covariance under the geometric definitions to ignore, emerge and compare detected landmarks. The experiment validates the effectiveness of the proposed filter in terms of detection precision and resource usage.

中文翻译：

---

Line–Circle–Square (LCS)：一种用于基于边缘检测的多层几何滤波器

本文提出了一种最先进的过滤器，可降低对象检测、跟踪和映射应用程序的复杂性。现有的边缘检测和跟踪方法被提出来为移动机器人创造合适的自主权，但是它们中的许多人在进入具有大量地标的场景的入口处面临过度自信和大量计算。特别是，仅仅依靠有限的传感器（例如相机）实际上效率不高。这项工作中的方法，即线-圆-方 (LCS) 过滤器，声称没有用于对象识别的大型数据库和高度先进的预测方法的移动机器人可以处理相机实时捕获的传入对象。建议的过滤器应用检测，对每个定义的专家进行跟踪和学习，以提取更多信息用于判断场景，而不会过度计算。每位专家之间的交互式学习源会产生最小的错误，从而对抗拥挤场景中的压倒性检测特征。我们的专家依赖于几何定义下信任因素的协方差来忽略、出现和比较检测到的地标。实验验证了所提出的过滤器在检测精度和资源使用方面的有效性。