Line-Circle: A Geometric Filter for Single Camera Edge-Based Object Detection

TL;DR

This paper introduces the Line-Circle filter, a geometric method for real-time, edge-based object detection using a single camera, enhancing SLAM applications without extensive databases or high computational costs.

Contribution

The proposed Line-Circle filter uniquely combines detection, tracking, and learning in a geometric framework to improve scene understanding in real-time without large databases.

Findings

Effective in crowded scenes without mapping

Reduces overconfidence and computation in SLAM

Works with minimal sensor data

Abstract

This paper presents a state-of-the-art approach in object detection for being applied in future SLAM problems. Although, many SLAM methods are proposed to create suitable autonomy for mobile robots namely ground vehicles, they still face overconfidence and large computations during entrance to immense spaces with many landmarks. In particular, they suffer from impractical applications via sole reliance on the limited sensors like camera. Proposed method claims that unmanned ground vehicles without having huge amount of database for object definition and highly advance prediction parameters can deal with incoming objects during straight motion of camera in real-time. Line-Circle (LC) filter tries to apply detection, tracking and learning to each defined experts to obtain more information for judging scene without over-calculation. In this filter, circle expert let us summarize edges in groups. The Interactive feedback learning between each expert creates minimal error that fights against overwhelming landmark signs in crowded scenes without mapping. Our experts basically are dependent on trust factors' covariance with geometric definitions to ignore, emerge and compare detected landmarks. The experiment for validating the model is taken place utilizing a camera beside an IMU sensor for location estimation.