Line-Circle-Square (LCS): A Multilayered Geometric Filter for Edge-Based Detection

TL;DR

The paper introduces the Line-Circle-Square (LCS) filter, a multilayered geometric approach that enhances real-time edge-based detection, tracking, and mapping for mobile robots with reduced computational complexity and improved landmark consistency.

Contribution

It presents a novel multilayered geometric filter that improves real-time object detection and tracking without extensive databases or advanced prediction, using interactive learning among experts.

Findings

Improves detection precision in crowded scenes

Reduces computational resource usage

Enhances landmark consistency through interactive learning

Abstract

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.