论文标题
线圈方（LCS）：用于基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
论文作者
论文摘要
本文提出了一种最新的过滤器，可降低对象检测，跟踪和映射应用程序中的复杂性。提出了现有的边缘检测和跟踪方法来为移动机器人创建合适的自主权，但是，其中许多人在场景的入口处面临过度自信和大型计算，并具有大量地标。这项工作的方法是线圈方（LCS）过滤器，声称没有大型数据库的移动机器人可以识别对象识别和高级预测方法，可以处理相机实时捕获的传入对象。所提出的过滤器将检测，跟踪和学习对每个定义的专家都应用，以提取更高级别的信息，以审判场景而不会过度计算。每个专家之间的互动学习提要增加了检测地标的一致性，这些地标与拥挤的场景中的压倒性检测功能作用。我们的专家取决于信托因素在几何定义下的协方差，以忽略，出现和比较被检测到的地标。该实验在实验和现实世界中的检测精度和资源使用方面验证了所提出的过滤器的有效性。
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.
