Leveraging line features to improve location accuracy of point-based visual-inertial SLAM (VINS) is gaining importance as they provide additional constraint of scene structureregularity, however, real-time performance  has not been focused . This paper presents PL-VINS, a real-time optimization-based monocular VINS method with point and line  , developed based on  state-of-the-art point-based VINS-Mono vins. Observe that current works use  LSD lsd algorithm to extract lines, however, the LSD is designed for scene shape representation instead of specific pose estimation problem, which becomes the bottleneck for the real-time performance due to its expensive cost. In this work , a modified LSD algorithm is presented by studying  hidden parameter tuning and length rejection strategy. The modified LSD can run three times at least as fast as the LSD. Further, by representing a line landmark with Pl\"{u}cker coordinate, the line reprojection residual is modeled as midpoint-to-line distance then minimized by iteratively updating the minimum four-parameter orthonormal representation of the Pl\"{u}cker coordinate . Experiments in public EuRoc benchmark dataset show the location error of our method is down 12-16\\%DIF < compared to VINS-Mono at the same work frequency on a low-power CPU @1.1 GHz without GPU parallelization. For the benefit of the community, we make public the source code: URL\end{abstract} %DIF > 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: URL
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 vins. We observe that current works use the LSD 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\\%DIF < compared to VINS-Mono at the same work frequency on a low-power CPU @1.1 GHz without GPU parallelization. For the benefit of the community, we make public the source code: URL\end{abstract} %DIF > 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: URL
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