Fast and Robust Monocular Visua-Inertial Odometry Using Points and Lines

Zhang, Ning; Zhao, Yongjia

doi:10.3390/s19204545

Cited by 6 publications

(3 citation statements)

References 42 publications

(50 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, the introduction of line and surface features increases the time consumption of feature extraction and matching, which reduces the efficiency of the SLAM system. Therefore, the VSLAM algorithm based on the point feature still occupies the mainstream position [92]. Table 4 shows a comparison of geometric features.…”

Section: Vslam Based On the Feature-based Methodsmentioning

confidence: 99%

An Overview on Visual SLAM: From Tradition to Semantic

Chen

Shang

et al. 2022

Remote Sensing

View full text Add to dashboard Cite

Visual SLAM (VSLAM) has been developing rapidly due to its advantages of low-cost sensors, the easy fusion of other sensors, and richer environmental information. Traditional visionbased SLAM research has made many achievements, but it may fail to achieve wished results in challenging environments. Deep learning has promoted the development of computer vision, and the combination of deep learning and SLAM has attracted more and more attention. Semantic information, as high-level environmental information, can enable robots to better understand the surrounding environment. This paper introduces the development of VSLAM technology from two aspects: traditional VSLAM and semantic VSLAM combined with deep learning. For traditional VSLAM, we summarize the advantages and disadvantages of indirect and direct methods in detail and give some classical VSLAM open-source algorithms. In addition, we focus on the development of semantic VSLAM based on deep learning. Starting with typical neural networks CNN and RNN, we summarize the improvement of neural networks for the VSLAM system in detail. Later, we focus on the help of target detection and semantic segmentation for VSLAM semantic information introduction. We believe that the development of the future intelligent era cannot be without the help of semantic technology. Introducing deep learning into the VSLAM system to provide semantic information can help robots better perceive the surrounding environment and provide people with higher-level help.

show abstract

Section: Vslam Based On the Feature-based Methodsmentioning

confidence: 99%

An Overview on Visual SLAM: From Tradition to Semantic

Chen

Shang

et al. 2022

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…The tracking of line features is extremely time-consuming and cannot meet the real-time requirements of the SLAM system. Therefore, point and line feature fusion has been applied to SLAM systems [ 16 , 17 , 18 , 19 , 20 , 21 , 22 ].…”

Section: Introductionmentioning

confidence: 99%

“…Direct tracking-based methods, such as large-scale direct monocular SLAM (LSD-SLAM) [5], direct sparse odometry (DSO) [6], and semi-direct monocular visual odometry (SVO) [7], perform estimation of the pose based on minimizing the photometric projection error. These methods are sensitive to illumination transformations and have poor differentiation between individual Sensors 2021, 21, 1196 2 of 20 pixels. In contrast, the indirect tracking-based method estimates a camera pose by tracking point features of the image.…”

Section: Introductionmentioning

confidence: 99%

A Multi-Feature Fusion Slam System Attaching Semantic Invariant to Points and Lines

Zeng

Huang

et al. 2021

Sensors

View full text Add to dashboard Cite

The traditional simultaneous localization and mapping (SLAM) system uses static points of the environment as features for real-time localization and mapping. When there are few available point features, the system is difficult to implement. A feasible solution is to introduce line features. In complex scenarios containing rich line segments, the description of line segments is not strongly differentiated, which can lead to incorrect association of line segment data, thus introducing errors into the system and aggravating the cumulative error of the system. To address this problem, a point-line stereo visual SLAM system incorporating semantic invariants is proposed in this paper. This system improves the accuracy of line feature matching by fusing line features with image semantic invariant information. When defining the error function, the semantic invariant is fused with the reprojection error function, and the semantic constraint is applied to reduce the cumulative error of the poses in the long-term tracking process. Experiments on the Office sequence of the TartanAir dataset and the KITTI dataset show that this system improves the matching accuracy of line features and suppresses the cumulative error of the SLAM system to some extent, and the mean relative pose error (RPE) is 1.38 and 0.0593 m, respectively.

show abstract

Look Behind You! – Using a Face Camera for Mobile Augmented Reality Odometry

Čejka¹,

Liarokapis²

2021

Digital Heritage. Progress in Cultural Heritage: Documentation, Preservation, and Protection

View full text Add to dashboard Cite

Fast and Robust Monocular Visua-Inertial Odometry Using Points and Lines

Cited by 6 publications

References 42 publications

An Overview on Visual SLAM: From Tradition to Semantic

An Overview on Visual SLAM: From Tradition to Semantic

A Multi-Feature Fusion Slam System Attaching Semantic Invariant to Points and Lines

Look Behind You! – Using a Face Camera for Mobile Augmented Reality Odometry

Contact Info

Product

Resources

About