Improved Point-Line Feature Based Visual SLAM Method for Complex Environments

Zhou, Fei; Zhang, Limin; Deng, Chaolong; Fan, Xinyue

doi:10.3390/s21134604

Cited by 20 publications

(12 citation statements)

References 40 publications

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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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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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“…Some researchers devote to this issue from different viewpoints. Points and line segments are combined to increase the mutuality between features in Zhou et al, 14 and thus obtaining a more accurate camera pose estimation. To obtain more environmental information, the feature extraction method based on color images is applied to visual SLAM in Wang et al 15 In Kim et al, 16 the matching risk is added to each feature point, and only feature points with lower matching risk are used in triangulation to avoid mismatching problems caused by excessive environmental changes.…”

Section: Introductionmentioning

confidence: 99%

Feature extraction and matching combined with depth information in visual simultaneous localization and mapping

Sun

2023

International Journal of Advanced Robotic Systems

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Estimating the camera trajectories is very important for the performance of visual simultaneous localization and mapping. However, visual simultaneous localization and mapping systems based on RGB image are generally not robust in complex situations such as low-textures or large illumination variations. In order to solve this problem, more environmental information is added by introducing depth information, and a feature extraction and matching algorithm combining depth information is proposed. In this article, firstly, the intrinsic mechanism that depth image is used to extract and match feature points is discussed. Then depth information and appearance information are comprehensively considered to extract and describe feature points. Finally, the matching problem of feature points is transformed into a regression and classification problem, with which a matching model is presented in a data-driven way. Experimental results show that our algorithm has better distribution uniformity and matching accuracy and can effectively improve the trajectory accuracy and drift degree of the simultaneous localization and mapping system.

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“…Feature-based SLAM depends on feature points and keyframes in order to build a map of an unknown environment, and vSLAM can achieve robust performance without requiring any noticeable changes in a static or rigid environment [ 1 ]. Direct SLAM uses all camera pixels to resolve the world around the sensor(s) based on principles from photogrammetry.…”

Section: Introductionmentioning

confidence: 99%

Visual SLAM for Dynamic Environments Based on Object Detection and Optical Flow for Dynamic Object Removal

Theodorou

Velisavljević

Dyo

2022

Sensors

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In dynamic indoor environments and for a Visual Simultaneous Localization and Mapping (vSLAM) system to operate, moving objects should be considered because they could affect the system’s visual odometer stability and its position estimation accuracy. vSLAM can use feature points or a sequence of images, as it is the only source of input that can perform localization while simultaneously creating a map of the environment. A vSLAM system based on ORB-SLAM3 and on YOLOR was proposed in this paper. The newly proposed system in combination with an object detection model (YOLOX) applied on extracted feature points is capable of achieving 2–4% better accuracy compared to VPS-SLAM and DS-SLAM. Static feature points such as signs and benches were used to calculate the camera position, and dynamic moving objects were eliminated by using the tracking thread. A specific custom personal dataset that includes indoor and outdoor RGB-D pictures of train stations, including dynamic objects and high density of people, ground truth data, sequence data, and video recordings of the train stations and X, Y, Z data was used to validate and evaluate the proposed method. The results show that ORB-SLAM3 with YOLOR as object detection achieves 89.54% of accuracy in dynamic indoor environments compared to previous systems such as VPS-SLAM.

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Improved Point-Line Feature Based Visual SLAM Method for Complex Environments

Cited by 20 publications

References 40 publications

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

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

Feature extraction and matching combined with depth information in visual simultaneous localization and mapping

Visual SLAM for Dynamic Environments Based on Object Detection and Optical Flow for Dynamic Object Removal

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