Incremental 3D Line Segment Extraction from Semi-dense SLAM

TL;DR

This paper introduces an efficient incremental method for extracting 3D line segments from semi-dense SLAM point clouds, improving scene representation and surface reconstruction accuracy.

Contribution

It presents a novel incremental approach that simplifies 3D line segment extraction by leveraging 2D fitting and combines image and depth data for accuracy.

Findings

High accuracy of generated 3D line segments

Enhanced 3D surface reconstruction quality

Efficient incremental extraction process

Abstract

Although semi-dense Simultaneous Localization and Mapping (SLAM) has been becoming more popular over the last few years, there is a lack of efficient methods for representing and processing their large scale point clouds. In this paper, we propose using 3D line segments to simplify the point clouds generated by semi-dense SLAM. Specifically, we present a novel incremental approach for 3D line segment extraction. This approach reduces a 3D line segment fitting problem into two 2D line segment fitting problems and takes advantage of both images and depth maps. In our method, 3D line segments are fitted incrementally along detected edge segments via minimizing fitting errors on two planes. By clustering the detected line segments, the resulting 3D representation of the scene achieves a good balance between compactness and completeness. Our experimental results show that the 3D line segments generated by our method are highly accurate. As an application, we demonstrate that these line segments greatly improve the quality of 3D surface reconstruction compared to a feature point based baseline.