PL-VINS: Real-Time Monocular Visual-Inertial SLAM with Point and Line Features

TL;DR

PL-VINS introduces a real-time monocular visual-inertial SLAM system that effectively integrates point and line features, utilizing a modified LSD algorithm for faster line extraction and improved localization accuracy.

Contribution

This work presents a novel real-time VINS method combining point and line features with a modified, faster LSD algorithm and a new line representation for enhanced accuracy.

Findings

Localization error reduced by 12-16% compared to VINS-Mono

Modified LSD runs at least three times faster

Improved scene structure constraints enhance accuracy

Abstract

Leveraging line features to improve localization accuracy of point-based visual-inertial SLAM (VINS) is gaining interest as they provide additional constraints on scene structure. However, real-time performance when incorporating line features in VINS has not been addressed. This paper presents PL-VINS, a real-time optimization-based monocular VINS method with point and line features, developed based on the state-of-the-art point-based VINS-Mono \cite{vins}. We observe that current works use the LSD \cite{lsd} algorithm to extract line features; however, LSD is designed for scene shape representation instead of the pose estimation problem, which becomes the bottleneck for the real-time performance due to its high computational cost. In this paper, a modified LSD algorithm is presented by studying a hidden parameter tuning and length rejection strategy. The modified LSD can run at least three times as fast as LSD. Further, by representing space lines with the Pl\"{u}cker coordinates, the residual error in line estimation is modeled in terms of the point-to-line distance, which is then minimized by iteratively updating the minimum four-parameter orthonormal representation of the Pl\"{u}cker coordinates. Experiments in a public benchmark dataset show that the localization error of our method is 12-16\% less than that of VINS-Mono at the same pose update frequency. %For the benefit of the community, The source code of our method is available at: https://github.com/cnqiangfu/PL-VINS.