A New Visual Inertial Simultaneous Localization and Mapping (SLAM) Algorithm Based on Point and Line Features

Zhang, Tong; Liu, Chunjiang; Li, Jiaqi; Pang, Minghui; Wang, Mingang

doi:10.3390/drones6010023

Cited by 15 publications

(10 citation statements)

References 16 publications

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“…Zhang et al proposed a point-line vision-based inertial localization method based on the PL-VIO algorithm [35]. A bilateral filtering algorithm and SURF feature extraction algorithm were adopted, combined with the fast nearest neighbor algorithm for feature matching.…”

Section: Related Workmentioning

confidence: 99%

Visual Odometry With Point and Line Features Based on Underground Tunnel Environment

Wang

et al. 2023

IEEE Access

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Compared with the interior environment, there exists a lot of noise and dust in an underground tunnel, and the light is unstable, which is difficult to track by the direct method. Moreover, errors in line segment projection and line feature drift under the influence of light can lead to significant deviations in the odometry. Consequently, to improve the accuracy and robustness of visual odometry, a point-line feature stereo visual odometry system is proposed in this paper. The system combines ORB features and LSD line features; using the angle relationship of line projection, we propose a new method for calculating the reprojection error of line features, reconstruct the reprojection model based on line features, and construct a new reprojection error model based on point-line features, which adds an angle constraint to the reprojection of line features and solves the instability caused by line projection error. It is shown in our experiments on the KITTI, New College dataset, that the translation error of our system is reduced by about 40% on average compared to PLVO, with a reduction in relative positioning error. Experiments in the hallway and underground tunnel environments have shown that the maximum positioning error of our system has been reduced by 75% in hallways and by 56.7% in an underground tunnel. Therefore, our algorithm effectively improves the localization accuracy and is more advantageous in low-texture environments.INDEX TERMS Visual odometry, point and line feature, underground tunnel, stereo

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Section: Related Workmentioning

confidence: 99%

Visual Odometry With Point and Line Features Based on Underground Tunnel Environment

Wang

et al. 2023

IEEE Access

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“…Compared with the single threshold set by experience in the paper [ 20 , 30 , 31 , 32 ], the threshold set in this paper is associated with the number of line segments extracted, image size and scene, which can more effectively adapt to the impact of different indoor scene changes.…”

Section: Point Line Feature Processingmentioning

confidence: 99%

PLI-VINS: Visual-Inertial SLAM Based on Point-Line Feature Fusion in Indoor Environment

Zhao

Song

Xing³

et al. 2022

Sensors

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In indoor low-texture environments, the point feature-based visual SLAM system has poor robustness and low trajectory accuracy. Therefore, we propose a visual inertial SLAM algorithm based on point-line feature fusion. Firstly, in order to improve the quality of the extracted line segment, a line segment extraction algorithm with adaptive threshold value is proposed. By constructing the adjacent matrix of the line segment and judging the direction of the line segment, it can decide whether to merge or eliminate other line segments. At the same time, geometric constraint line feature matching is considered to improve the efficiency of processing line features. Compared with the traditional algorithm, the processing efficiency of our proposed method is greatly improved. Then, point, line, and inertial data are effectively fused in a sliding window to achieve high-accuracy pose estimation. Finally, experiments on the EuRoC dataset show that the proposed PLI-VINS performs better than the traditional visual inertial SLAM system using point features and point line features.

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“…In feature point-based methods, in order to make full use of environmental characteristic information and improve environmental adaptability, line features are added in [5,13,14,[20][21][22][23]. For example, PL-VIO [5] adds the reprojection error and a priori information into the back end's optimization based on the VINS-MONO theory.…”

Section: Related Workmentioning

confidence: 99%

“…However, their high time cost makes it hard to achieve real-time tracking. In Zhang et al's work [23], the authors adjust the internal parameters of LSD, such as the pyramid number, scale factor and minimum density threshold, to improve the extraction speed. It is true that the speed is improved a lot, but the extraction quality is difficult to guarantee, and these parameters may be affected by the image size.…”

Section: Related Workmentioning

confidence: 99%

“…Since the direction of the spatial straight line projection to the pixel plane is only related to the normal vector n, the formula for projecting a spatially straight line to the pixel plane is as per equations ( 22) and (23). The reprojection error is calculated only for the distance between the line projected to the pixel plane l ci l and the observed endpoints s ci l , e ci l in c i image.…”

Section: Line Feature Measurement Modelmentioning

confidence: 99%

See 1 more Smart Citation

Fast visual inertial odometry with point–line features using adaptive EDLines algorithm

Zhao¹,

Zhang²,

Wei³

2022

Meas. Sci. Technol.

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In mainstream visual inertial odometry systems, the method of positional solution by feature point extraction and matching in the image is widely used. But the tracking accuracy of point features is dependent on texture richness in the environment. Although many existing algorithms introduce line features in the front-end to improve the system's environmental adaptability, most of them sacrifice system real-time in exchange for higher positioning accuracy. The extraction and matching of line features often require more time, thus failing to meet the real-time requirements of the system for localization. In this paper, therefore, we propose a fast visual inertial odometry fused with point and line features, which enables the system to maintain a high level of positioning robustness in dim and changing light environments with low time cost. The point-line features visual inertial odometry algorithm is based on adaptive thresholding of EDLines. By adding an adaptive thresholding component to the EDLines algorithm, the robustness of line feature extraction is enhanced to better adapt to changes in ambient lighting. And the time for line feature extraction is significantly reduced. And a line feature matching algorithm based on geometric information and structural similarity is proposed, which enables fast and accurate line features matching. The algorithm is compared with PL-VIO and VINS-MONO algorithms on the EuRoc dataset and real world secens. Many experiments prove that the algorithm has improved in both real-time and accuracy.

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A New Visual Inertial Simultaneous Localization and Mapping (SLAM) Algorithm Based on Point and Line Features

Cited by 15 publications

References 16 publications

Visual Odometry With Point and Line Features Based on Underground Tunnel Environment

Visual Odometry With Point and Line Features Based on Underground Tunnel Environment

PLI-VINS: Visual-Inertial SLAM Based on Point-Line Feature Fusion in Indoor Environment

Fast visual inertial odometry with point–line features using adaptive EDLines algorithm

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