PLD-VINS: RGBD visual-inertial SLAM with point and line features

Zhu, Yeqing; Jin, Rui; Lou, Taishan; Zhao, Liangyu

doi:10.1016/j.ast.2021.107185

Cited by 17 publications

(2 citation statements)

References 26 publications

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“…Alternatively, frontend feature extraction can include other primitive features in the visual scene to solve the few-point-feature problem in lowtexture scenario. PLD-VINS (Zhu et al, 2021) uses point features, line features, and depth information, while PL-VINS (Q. is based on VINS-Mono and uses point and line features, both with improved accuracy over VINS-Mono.…”

Section: Viomentioning

confidence: 99%

Real-Time Uav 3d Image Point Clouds Mapping

Sun,

Chen,

Wang

et al. 2023

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. This paper proposes a real-time 3D image point clouds mapping algorithm for UAVs that is capable of mapping effectively in weak GNSS environments. And a UAV mapping system is integrated with a RGB camera, an inertial measurement unit (IMU), a GNSS receiver, data transmission devices, and a DJI M300 flight platform. To achieve real-time and robust mapping, the system utilizes a visual-inertial odometry (VIO) that tightly couples GNSS, RGB image, and IMU, which provides stable state estimation information for mapping. Subsequently, a dense matching algorithm based on key frames is adopted to recover 3D mapping information with low-computational cost. Extensive experiments are conducted on our test site, demonstrating the system's ability to build maps stably, even under the effect of wind. The results compared with the trajectory reconstructed by Pix4D show that the system achieves competitive accuracy of pose estimation and is capable of real-time mapping.

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Section: Viomentioning

confidence: 99%

Real-Time Uav 3d Image Point Clouds Mapping

Sun,

Chen,

Wang

et al. 2023

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…PI-VIO, PL-VIO, and PL-SLAM [8,11,12] focus on the improvement of a system's accuracy and robustness while ignoring real-time indoor localization. PLD-VINS [13], on the other hand, uses the EDLines algorithm for feature extraction, which improves line feature detection efficiency. Structure SLAM [14] decouples rotation and translation estimation of the tracking process to reduce the long-term drift in indoor environments.…”

Section: Introductionmentioning

confidence: 99%

ULG-SLAM: A Novel Unsupervised Learning and Geometric Feature-Based Visual SLAM Algorithm for Robot Localizability Estimation

Huang,

Xie,

Zhao

et al. 2024

Remote Sensing

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Indoor localization has long been a challenging task due to the complexity and dynamism of indoor environments. This paper proposes ULG-SLAM, a novel unsupervised learning and geometric-based visual SLAM algorithm for robot localizability estimation to improve the accuracy and robustness of visual SLAM. Firstly, a dynamic feature filtering based on unsupervised learning and moving consistency checks is developed to eliminate the features of dynamic objects. Secondly, an improved line feature extraction algorithm based on LSD is proposed to optimize the effect of geometric feature extraction. Thirdly, geometric features are used to optimize localizability estimation, and an adaptive weight model and attention mechanism are built using the method of region delimitation and region growth. Finally, to verify the effectiveness and robustness of localizability estimation, multiple indoor experiments using the EuRoC dataset and TUM RGB-D dataset are conducted. Compared with ORBSLAM2, the experimental results demonstrate that absolute trajectory accuracy can be improved by 95% for equivalent processing speed in walking sequences. In fr3/walking_xyz and fr3/walking_half, ULG-SLAM tracks more trajectories than DS-SLAM, and the ATE RMSE is improved by 36% and 6%, respectively. Furthermore, the improvement in robot localizability over DynaSLAM is noteworthy, coming in at about 11% and 3%, respectively.

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