线-圆-方（LCS）：一种用于边缘检测的多层几何滤波器
Line-Circle-Square (LCS): A Multilayered Geometric Filter for Edge-Based Detection
摘要：
本文提出了一个国家的最先进的过滤器，降低了复杂性，在目标检测，跟踪和映射应用。现有的边缘检测和跟踪方法被提出来为移动机器人创建合适的自主性，然而，其中许多方法在具有大量地标的场景入口处面临过度自信和大量计算。这项工作中的方法，即线性-圆形-方形（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。该滤波器对每个专家进行检测、跟踪和学习，提取更高层次的信息用于场景判断，避免了过度计算。每个专家之间的交互式学习反馈增加了检测到的地标的一致性，这对拥挤场景中压倒性的检测到的特征起作用。我们的专家依赖于几何定义下的信任因子的协方差来忽略、出现和比较检测到的地标。实验验证了该过滤器在检测精度和资源使用方面的有效性，在实验和现实世界的场景。
英文摘要：
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. 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 higher level information for judging scenes without over-calculation. The interactive learning feed between each expert increases the consistency of detected landmarks 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 in both experimental and real-world scenarios.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
