2D Line Matching Using Geometric and Intensity Data

Woo, Dong-Min; Park, Dong-Chul; Han, Seung-Soo; Beack, Seunghwa

doi:10.1109/aici.2009.287

Cited by 9 publications

(6 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Based on the LBD line segment descriptor, we introduce the geometric properties [15] of line to perform effective line matching. In our approach, two successfully matched line features l 1 , l 2 need to satisfy the following conditions: 1) the angular difference of two matched lines is smaller than a given threshold Φ; 2) the length of l 1 , l 1 is similar to the length of l 2 , l 2 : min( l1 , l2 ) max( l1 , l2 ) > τ ; 3) the overlapping length of the two lines is greater than a certain value: 4) the distance between the two LBD descriptors is less than a certain value.…”

Section: B Line Feature Matchingmentioning

confidence: 99%

See 1 more Smart Citation

Robust visual SLAM with point and line features

Zuo

Xie

Liu

et al. 2017

2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

139

View full text Add to dashboard Cite

Abstract-In this paper, we develop a robust efficient visual SLAM system that utilizes heterogeneous point and line features. By leveraging ORB-SLAM [1], the proposed system consists of stereo matching, frame tracking, local mapping, loop detection, and bundle adjustment of both point and line features. In particular, as the main theoretical contributions of this paper, we, for the first time, employ the orthonormal representation as the minimal parameterization to model line features along with point features in visual SLAM and analytically derive the Jacobians of the re-projection errors with respect to the line parameters, which significantly improves the SLAM solution. The proposed SLAM has been extensively tested in both synthetic and real-world experiments whose results demonstrate that the proposed system outperforms the state-of-the-art methods in various scenarios.

show abstract

Section: B Line Feature Matchingmentioning

confidence: 99%

Section: B Line Feature Matchingmentioning

confidence: 99%

Robust visual SLAM with point and line features

Zuo

Xie

Liu

et al. 2017

2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

139

View full text Add to dashboard Cite

show abstract

“…On the other hand, the images are collected by the stereo camera; after performing the feature detection and initialization which will be discussed in the next subsection, the error-state and measurement equations can be established by (22), (23), (24), (25), (26), (27), (28), (29), (30), (31), (32), (33), (34), (35), (36), (37), and (38) with the IMU measurement and the update result of the inertial navigation. In the last step, the EKF estimator is executed by (39) and (40). In addition, the covariance matrix of the EKF should be augmented when new point or line features are observed.…”

Section: Journal Of Sensorsmentioning

confidence: 99%

“…The Hamming distance is used to measure the similarity of point features with a predefined threshold. For the line feature, we exploit the matching method proposed in [40].…”

Section: Feature Detection and Initializationmentioning

confidence: 99%

An Indoor Navigation System Based on Stereo Camera and Inertial Sensors with Points and Lines

Yang

Zhang

et al. 2018

Journal of Sensors

View full text Add to dashboard Cite

An indoor navigation system based on stereo camera and inertial sensors with points and lines is proposed to further improve the accuracy and robustness of the navigation system in complex indoor environments. The point and line features, which are fast extracted by ORB method and line segment detector (LSD) method, are both employed in this system to improve its ability to adapt to complex environments. In addition, two different representations of lines are adopted to improve the efficiency of the system. Besides stereo camera, an inertial measurement unit (IMU) is also used in the system to further improve its accuracy and robustness. An estimator is designed to integrate the camera and IMU measurements in a tightly coupled approach. The experimental results show that the performance of the proposed navigation system is better than the point-only VINS and the vision-only navigation system with points and lines.

show abstract

“…In addition, it is extremely timeconsuming to extract lines and calculate the corresponding descriptors for each image. Geometry-based approaches [7]- [11] extract a sparse set of line features in each image and predict each line in new image at first, and then track each line by searching for the extracted line in new image that best satisfies the corresponding geometric constraints with the predicted one. Especially, [10] [11] provide better predictive value by using point tracking method, such as KLT [12], to track the sampled points on line.…”

Section: Introductionmentioning

confidence: 99%

LOF: Structure-Aware Line Tracking based on Optical Flow

Quan¹,

Chai²,

Liu³

2021

Preprint

View full text Add to dashboard Cite

Lines provide the significantly richer geometric structural information about the environment than points, so lines are widely used in recent Visual Odometry (VO) works. Since VO with lines use line tracking results to locate and map, line tracking is a crucial component in VO. Although the stateof-the-art line tracking methods have made great progress, they are still heavily dependent on line detection or the predicted line segments. In order to relieve the dependencies described above to track line segments completely, accurately, and robustly at higher computational efficiency, we propose a structure-aware Line tracking algorithm based entirely on Optical Flow (LOF). Firstly, we propose a gradient-based strategy to sample pixels on lines that are suitable for line optical flow calculation. Then, in order to align the lines by fully using the structural relationship between the sampled points on it and effectively removing the influence of sampled points on it occluded by other objects, we propose a two-step structure-aware line segment alignment method. Furthermore, we propose a line refinement method to refine the orientation, position, and endpoints of the aligned line segments. Extensive experimental results demonstrate that the proposed LOF outperforms the state-of-the-art performance in line tracking accuracy, robustness, and efficiency, which also improves the location accuracy and robustness of VO system with lines.

show abstract

2D Line Matching Using Geometric and Intensity Data

Cited by 9 publications

References 9 publications

Robust visual SLAM with point and line features

Robust visual SLAM with point and line features

An Indoor Navigation System Based on Stereo Camera and Inertial Sensors with Points and Lines

LOF: Structure-Aware Line Tracking based on Optical Flow

Contact Info

Product

Resources

About