Robust Visual SLAM with Point and Line Features

TL;DR

This paper presents a robust visual SLAM system that integrates point and line features, employing a novel minimal parameterization for lines to enhance accuracy and performance in diverse environments.

Contribution

It introduces the first use of orthonormal representation for line features in visual SLAM, improving the SLAM solution's robustness and accuracy.

Findings

Outperforms state-of-the-art methods in synthetic and real-world tests

Significantly improves SLAM accuracy with line feature modeling

Demonstrates robustness in various challenging scenarios

Abstract

In this paper, we develop a robust efficient visual SLAM system that utilizes heterogeneous point and line features. By leveraging ORB-SLAM [1], the proposed system consists of stereo matching, frame tracking, local mapping, loop detection, and bundle adjustment of both point and line features. In particular, as the main theoretical contributions of this paper, we, for the first time, employ the orthonormal representation as the minimal parameterization to model line features along with point features in visual SLAM and analytically derive the Jacobians of the re-projection errors with respect to the line parameters, which significantly improves the SLAM solution. The proposed SLAM has been extensively tested in both synthetic and real-world experiments whose results demonstrate that the proposed system outperforms the state-of-the-art methods in various scenarios.