Stereo Plane SLAM Based on Intersecting Lines

TL;DR

This paper introduces a novel stereo SLAM method that extracts plane features from intersecting lines in stereo images, improving accuracy and reducing drift in SLAM systems, especially for man-made environments.

Contribution

It proposes a new approach to compute plane parameters from intersecting lines in stereo images, enhancing plane-based SLAM performance.

Findings

Reduces drift error in SLAM systems.

Demonstrates robust and accurate results on public datasets.

Outperforms state-of-the-art SLAM methods.

Abstract

Plane feature is a kind of stable landmark to reduce drift error in SLAM system. It is easy and fast to extract planes from dense point cloud, which is commonly acquired from RGB-D camera or lidar. But for stereo camera, it is hard to compute dense point cloud accurately and efficiently. In this paper, we propose a novel method to compute plane parameters using intersecting lines which are extracted from the stereo image. The plane features commonly exist on the surface of man-made objects and structure, which have regular shape and straight edge lines. In 3D space, two intersecting lines can determine such a plane. Thus we extract line segments from both stereo left and right image. By stereo matching, we compute the endpoints and line directions in 3D space, and then the planes from two intersecting lines. We discard those inaccurate plane features in the frame tracking. Adding such plane features in stereo SLAM system reduces the drift error and refines the performance. We test our proposed system on public datasets and demonstrate its robust and accurate estimation results, compared with state-of-the-art SLAM systems. To benefit the research of plane-based SLAM, we release our codes at https://github.com/fishmarch/Stereo-Plane-SLAM.