Robust RGB-D SLAM Using Point and Line Features for Low Textured Scene

Zou, Yajing; Eldemiry, Amr; Li, Yaxin; Chen, Wu

doi:10.3390/s20174984

Cited by 15 publications

(16 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We ran each sequence seven times and show the average result of trajectory accuracy estimation. The evaluation indices used in the experiment were absolute trajectory error (ATE), and root means square error (RMSE) [ 34 ].…”

Section: Experiments and Discussionmentioning

confidence: 99%

IT-SVO: Improved Semi-Direct Monocular Visual Odometry Combined with JS Divergence in Restricted Mobile Devices

Liu

Zhao

Sun

et al. 2021

Sensors

View full text Add to dashboard Cite

Simultaneous localization and mapping (SLAM) has a wide range for applications in mobile robotics. Lightweight and inexpensive vision sensors have been widely used for localization in GPS-denied or weak GPS environments. Mobile robots not only estimate their pose, but also correct their position according to the environment, so a proper mathematical model is required to obtain the state of robots in their circumstances. Usually, filter-based SLAM/VO regards the model as a Gaussian distribution in the mapping thread, which deals with the complicated relationship between mean and covariance. The covariance in SLAM or VO represents the uncertainty of map points. Therefore, the methods, such as probability theory and information theory play a significant role in estimating the uncertainty. In this paper, we combine information theory with classical visual odometry (SVO) and take Jensen-Shannon divergence (JS divergence) instead of Kullback-Leibler divergence (KL divergence) to estimate the uncertainty of depth. A more suitable methodology for SVO is that explores to improve the accuracy and robustness of mobile devices in unknown environments. Meanwhile, this paper aims to efficiently utilize small portability for location and provide a priori knowledge of the latter application scenario. Therefore, combined with SVO, JS divergence is implemented, which has been realized. It not only has the property of accurate distinction of outliers, but also converges the inliers quickly. Simultaneously, the results show, under the same computational simulation, that SVO combined with JS divergence can more accurately locate its state in the environment than the combination with KL divergence.

show abstract

Section: Experiments and Discussionmentioning

confidence: 99%

IT-SVO: Improved Semi-Direct Monocular Visual Odometry Combined with JS Divergence in Restricted Mobile Devices

Liu

Zhao

Sun

et al. 2021

Sensors

View full text Add to dashboard Cite

show abstract

“…It is mentioned that the visual SLAM with point features is easy to fail in low textured scenes. As it is quite convenient to use Pliicker coordinates to represent lines, a robust RGB-D SLAM using point-line features for low scenes was proposed [47] well. Although RGB-D camera can obtain depth information, these RGB-D SLAM systems above only utilized 2D points and lines (i.e., the 2D re-projection error of point and line features), and the 3D points and lines acquired from depth image are not used for estimation pose.…”

Section: A Related Workmentioning

confidence: 99%

“…Although RGB-D camera can obtain depth information, these RGB-D SLAM systems above only utilized 2D points and lines (i.e., the 2D re-projection error of point and line features), and the 3D points and lines acquired from depth image are not used for estimation pose. Besides, the depth information either is directly used or not taken into full consideration in the SLAM systems [44,47].…”

Section: A Related Workmentioning

confidence: 99%

PL-GM:RGB-D SLAM With a Novel 2D and 3D Geometric Constraint Model of Point and Line Features

Zhang

2021

IEEE Access

View full text Add to dashboard Cite

In the study of RGB-D SLAM (Simultaneous Localization and Mapping), two types of primary visual features, point and line features, have been widely utilized to calculate the camera pose. As an RGB-D camera can capture RGB and depth information simultaneously, most RGB-D SLAM methods only utilize the 2D information within the point and line features. To obtain a higher accuracy camera pose and utilize the 2D and 3D information within points and lines better, a novel geometric constraint model of points and lines (PL-GM) using an RGB-D camera is proposed in this paper. Our contributions are threefold. Firstly, the 3D points and lines generated by an RGB-D camera combining with 2D point and line features are utilized to establish the PL-GM, which is different from most models of point-line SLAM (PL-SLAM). Secondly, in addition to the 2D re-projection error of point and line features, the constraint errors of 3D points and lines are constructed and minimized likewise, and then a unified optimization model based on PL-GM is extended to the bundle adjustment model (BA). Finally, extensive experiments have been performed on two public benchmark RGB-D datasets and a real scenario sequence. These experimental results demonstrate that our method achieves a comparable or better performance than the state-of-the-art SLAM methods based on point and line features, and point features.

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%

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

Robust RGB-D SLAM Using Point and Line Features for Low Textured Scene

Cited by 15 publications

References 42 publications

IT-SVO: Improved Semi-Direct Monocular Visual Odometry Combined with JS Divergence in Restricted Mobile Devices

IT-SVO: Improved Semi-Direct Monocular Visual Odometry Combined with JS Divergence in Restricted Mobile Devices

PL-GM:RGB-D SLAM With a Novel 2D and 3D Geometric Constraint Model of Point and Line Features

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

Contact Info

Product

Resources

About