scholarly journals Point-Line Visual Stereo SLAM Using EDlines and PL-BoW

2021 ◽  
Vol 13 (18) ◽  
pp. 3591
Author(s):  
Hanxiao Rong ◽  
Yanbin Gao ◽  
Lianwu Guan ◽  
Alex Ramirez-Serrano ◽  
Xu Xu ◽  
...  
Keyword(s):  
Feature Detection ◽  
Geometric Constraints ◽  
Spatial Proximity ◽  
Visual Words ◽  
Model Combining ◽  
Localization And Mapping ◽  
Loop Detection ◽  
Bow Technique ◽  
Line Features ◽  
Point Line

Visual Simultaneous Localization and Mapping (SLAM) technologies based on point features achieve high positioning accuracy and complete map construction. However, despite their time efficiency and accuracy, such SLAM systems are prone to instability and even failure in poor texture environments. In this paper, line features are integrated with point features to enhance the robustness and reliability of stereo SLAM systems in poor texture environments. Firstly, method Edge Drawing lines (EDlines) is applied to reduce the line feature detection time. Meanwhile, the proposed method improves the reliability of features by eliminating outliers of line features based on the entropy scale and geometric constraints. Furthermore, this paper proposes a novel Bags of Word (BoW) model combining the point and line features to improve the accuracy and robustness of loop detection used in SLAM. The proposed PL-BoW technique achieves this by taking into account the co-occurrence information and spatial proximity of visual words. Experiments using the KITTI and EuRoC datasets demonstrate that the proposed stereo Point and EDlines SLAM (PEL-SLAM) achieves high accuracy consistently, including in challenging environments difficult to sense accurately. The processing time of the proposed method is reduced by 9.9% and 4.5% when compared to the Point and Line SLAM (PL-SLAM) and Point and stereo Point and Line based Visual Odometry (sPLVO) methods, respectively.

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

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1196
Author(s):  
Gang Li ◽  
Yawen Zeng ◽  
Huilan Huang ◽  
Shaojian Song ◽  
Bin Liu ◽  
...  
Keyword(s):  
Feature Matching ◽  
Feature Fusion ◽  
Error Function ◽  
Line Segments ◽  
Cumulative Error ◽  
Localization And Mapping ◽  
Tracking Process ◽  
Line Features ◽  
Point Line ◽  
Segment Data

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.

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

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4604
Author(s):  
Fei Zhou ◽  
Limin Zhang ◽  
Chaolong Deng ◽  
Xinyue Fan
Keyword(s):  
Visual Slam ◽  
Complex Environments ◽  
Line Segments ◽  
Structure Information ◽  
Line Feature ◽  
Localization And Mapping ◽  
Feature Based ◽  
Line Features ◽  
Point Line ◽  
Local Adaptive Threshold

Traditional visual simultaneous localization and mapping (SLAM) systems rely on point features to estimate camera trajectories. However, feature-based systems are usually not robust in complex environments such as weak textures or obvious brightness changes. To solve this problem, we used more environmental structure information by introducing line segments features and designed a monocular visual SLAM system. This system combines points and line segments to effectively make up for the shortcomings of traditional positioning based only on point features. First, ORB algorithm based on local adaptive threshold was proposed. Subsequently, we not only optimized the extracted line features, but also added a screening step before the traditional descriptor matching to combine the point features matching results with the line features matching. Finally, the weighting idea was introduced. When constructing the optimized cost function, we allocated weights reasonably according to the richness and dispersion of features. Our evaluation on publicly available datasets demonstrated that the improved point-line feature method is competitive with the state-of-the-art methods. In addition, the trajectory graph significantly reduced drift and loss, which proves that our system increases the robustness of SLAM.

Vision and laser fused SLAM in indoor environments with multi-robot system

2019 ◽  
Vol 39 (2) ◽  
pp. 297-307 ◽  
Author(s):  
Haoyao Chen ◽  
Hailin Huang ◽  
Ye Qin ◽  
Yanjie Li ◽  
Yunhui Liu
Keyword(s):  
Large Scale ◽  
Feature Detection ◽  
Mobile Robotics ◽  
Least Square ◽  
Indoor Environments ◽  
Fusion Algorithm ◽  
Content Type ◽  
Localization And Mapping ◽  
Loop Detection ◽  
Multi Robot

Purpose Multi-robot laser-based simultaneous localization and mapping (SLAM) in large-scale environments is an essential but challenging issue in mobile robotics, especially in situations wherein no prior knowledge is available between robots. Moreover, the cumulative errors of every individual robot exert a serious negative effect on loop detection and map fusion. To address these problems, this paper aims to propose an efficient approach that combines laser and vision measurements. Design/methodology/approach A multi-robot visual laser-SLAM is developed to realize robust and efficient SLAM in large-scale environments; both vision and laser loop detections are integrated to detect robust loops. A method based on oriented brief (ORB) feature detection and bag of words (BoW) is developed, to ensure the robustness and computational effectiveness of the multi-robot SLAM system. A robust and efficient graph fusion algorithm is proposed to merge pose graphs from different robots. Findings The proposed method can detect loops more quickly and accurately than the laser-only SLAM, and it can fuse the submaps of each single robot to promote the efficiency, accuracy and robustness of the system. Originality/value Compared with the state of art of multi-robot SLAM approaches, the paper proposed a novel and more sophisticated approach. The vision-based and laser-based loops are integrated to realize a robust loop detection. The ORB features and BoW technologies are further utilized to gain real-time performance. Finally, random sample consensus and least-square methodologies are used to remove the outlier loops among robots.

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

Drones ◽  
2022 ◽  
Vol 6 (1) ◽  
pp. 23
Author(s):  
Tong Zhang ◽  
Chunjiang Liu ◽  
Jiaqi Li ◽  
Minghui Pang ◽  
Mingang Wang
Keyword(s):  
Feature Extraction ◽  
Real Time ◽  
Simultaneous Localization And Mapping ◽  
Line Feature ◽  
Speeded Up Robust Features ◽  
Localization And Mapping ◽  
Line Features ◽  
Point Line ◽  
Point Feature ◽  
Weak Texture

In view of traditional point-line feature visual inertial simultaneous localization and mapping (SLAM) system, which has weak performance in accuracy so that it cannot be processed in real time under the condition of weak indoor texture and light and shade change, this paper proposes an inertial SLAM method based on point-line vision for indoor weak texture and illumination. Firstly, based on Bilateral Filtering, we apply the Speeded Up Robust Features (SURF) point feature extraction and Fast Nearest neighbor (FLANN) algorithms to improve the robustness of point feature extraction result. Secondly, we establish a minimum density threshold and length suppression parameter selection strategy of line feature, and take the geometric constraint line feature matching into consideration to improve the efficiency of processing line feature. And the parameters and biases of visual inertia are initialized based on maximum posterior estimation method. Finally, the simulation experiments are compared with the traditional tightly-coupled monocular visual–inertial odometry using point and line features (PL-VIO) algorithm. The simulation results demonstrate that the proposed an inertial SLAM method based on point-line vision for indoor weak texture and illumination can be effectively operated in real time, and its positioning accuracy is 22% higher on average and 40% higher in the scenario that illumination changes and blurred image.

scholarly journals Affine-Invariant Geometric Constraints-Based High Accuracy Simultaneous Localization and Mapping

2017 ◽  
Vol 2017 ◽  
pp. 1-7
Author(s):  
Gangchen Hua ◽  
Xu Tan
Keyword(s):  
Large Scale ◽  
Simultaneous Localization And Mapping ◽  
Geometric Constraints ◽  
Long Term Memory ◽  
Affine Invariant ◽  
Loop Closure ◽  
Visual Words ◽  
Loop Closure Detection ◽  
Localization And Mapping

In this study we describe a new appearance-based loop-closure detection method for online incremental simultaneous localization and mapping (SLAM) using affine-invariant-based geometric constraints. Unlike other pure bag-of-words-based approaches, our proposed method uses geometric constraints as a supplement to improve accuracy. By establishing an affine-invariant hypothesis, the proposed method excludes incorrect visual words and calculates the dispersion of correctly matched visual words to improve the accuracy of the likelihood calculation. In addition, camera’s intrinsic parameters and distortion coefficients are adequate for this method. 3D measuring is not necessary. We use the mechanism of Long-Term Memory and Working Memory (WM) to manage the memory. Only a limited size of the WM is used for loop-closure detection; therefore the proposed method is suitable for large-scale real-time SLAM. We tested our method using the CityCenter and Lip6Indoor datasets. Our proposed method results can effectively correct the typical false-positive localization of previous methods, thus gaining better recall ratios and better precision.

A computer vision approach to identifying the manufacturer and model of anterior cervical spinal hardware

2019 ◽  
Vol 31 (6) ◽  
pp. 844-850 ◽  
Author(s):  
Kevin T. Huang ◽  
Michael A. Silva ◽  
Alfred P. See ◽  
Kyle C. Wu ◽  
Troy Gallerani ◽  
...  
Keyword(s):  
Computer Vision ◽  
Feature Detection ◽  
High Accuracy ◽  
Detection Accuracy ◽  
Data Sets ◽  
Visual Words ◽  
Fusion Systems ◽  
Kaze Feature ◽  
Applications Of Machine Learning ◽  
Cervical Plating

OBJECTIVERecent advances in computer vision have revolutionized many aspects of society but have yet to find significant penetrance in neurosurgery. One proposed use for this technology is to aid in the identification of implanted spinal hardware. In revision operations, knowing the manufacturer and model of previously implanted fusion systems upfront can facilitate a faster and safer procedure, but this information is frequently unavailable or incomplete. The authors present one approach for the automated, high-accuracy classification of anterior cervical hardware fusion systems using computer vision.METHODSPatient records were searched for those who underwent anterior-posterior (AP) cervical radiography following anterior cervical discectomy and fusion (ACDF) at the authors’ institution over a 10-year period (2008–2018). These images were then cropped and windowed to include just the cervical plating system. Images were then labeled with the appropriate manufacturer and system according to the operative record. A computer vision classifier was then constructed using the bag-of-visual-words technique and KAZE feature detection. Accuracy and validity were tested using an 80%/20% training/testing pseudorandom split over 100 iterations.RESULTSA total of 321 total images were isolated containing 9 different ACDF systems from 5 different companies. The correct system was identified as the top choice in 91.5% ± 3.8% of the cases and one of the top 2 or 3 choices in 97.1% ± 2.0% and 98.4 ± 13% of the cases, respectively. Performance persisted despite the inclusion of variable sizes of hardware (i.e., 1-level, 2-level, and 3-level plates). Stratification by the size of hardware did not improve performance.CONCLUSIONSA computer vision algorithm was trained to classify at least 9 different types of anterior cervical fusion systems using relatively sparse data sets and was demonstrated to perform with high accuracy. This represents one of many potential clinical applications of machine learning and computer vision in neurosurgical practice.

scholarly journals A visual SLAM method based on point-line fusion in weak-matching scene

2020 ◽  
Vol 17 (2) ◽  
pp. 172988142090419 ◽  
Author(s):  
Baofu Fang ◽  
Zhiqiang Zhan
Keyword(s):  
State Of The Art ◽  
Localization Accuracy ◽  
Line Segments ◽  
Research Areas ◽  
Current State ◽  
Localization And Mapping ◽  
Reprojection Error ◽  
Feature Based ◽  
Point Line ◽  
Point Feature

Visual simultaneous localization and mapping (SLAM) is well-known to be one of the research areas in robotics. There are many challenges in traditional point feature-based approaches, such as insufficient point features, motion jitter, and low localization accuracy in low-texture scenes, which reduce the performance of the algorithms. In this article, we propose an RGB-D SLAM system to handle these situations, which is named Point-Line Fusion (PLF)-SLAM. We utilize both points and line segments throughout the process of our work. Specifically, we present a new line segment extraction method to solve the overlap or branch problem of the line segments, and then a more rigorous screening mechanism is proposed in the line matching section. Instead of minimizing the reprojection error of points, we introduce the reprojection error based on points and lines to get a more accurate tracking pose. In addition, we come up with a solution to handle the jitter frame, which greatly improves tracking success rate and availability of the system. We thoroughly evaluate our system on the Technische Universität München (TUM) RGB-D benchmark and compare it with ORB-SLAM2, presumably the current state-of-the-art solution. The experiments show that our system has better accuracy and robustness compared to the ORB-SLAM2.

A Comparison of Feature Extraction Algorithms Based on Sonar Sensor Data

2013 ◽  
Author(s):  
Hesham Ismail ◽  
Balakumar Balachandran
Keyword(s):  
Feature Extraction ◽  
Feature Detection ◽  
Industrial Applications ◽  
Sensor Data ◽  
Mobile Platforms ◽  
Fusion Algorithm ◽  
Unknown Environment ◽  
Mobile Vehicle ◽  
Localization And Mapping ◽  
Ongoing Work

Simultaneous localization and mapping (SLAM) is a technique used to determine the location of a mobile vehicle in an unknown environment, while constructing a map of the unknown environment at the same time. Mobile platforms, which make use of SLAM algorithms, have industrial applications in autonomous maintenance, such as the inspection of flaws and defects in oil pipelines and storage tanks. An important component of SLAM is feature extraction, which is the process of detecting and extracting significant features such as corners, edges, and walls in an environment. Here, the use of sonars as sensors mounted on a mobile platform is examined, and a comparison of different algorithms currently in use is made and presented. This comparison is performed through a combination of experimental and numerical studies. The triangulation-based fusion algorithm is examined for point feature detection, and the standard Hough Transform and the triangulation Hough fusion (THF) are used for line detection. Comparisons are discussed and presented along with ongoing work.

scholarly journals Robust Landmark Estimation and Unscented Particle Sampling for SLAM in Dynamic Outdoor Environment

2010 ◽  
Vol 22 (2) ◽  
pp. 140-149 ◽  
Author(s):  
Atsushi Sakai ◽  
◽  
Teppei Saitoh ◽  
Yoji Kuroda
Keyword(s):  
Data Management ◽  
Feature Detection ◽  
Data Association ◽  
Position Estimation ◽  
Outdoor Environment ◽  
Unscented Transformation ◽  
Particle Sampling ◽  
Localization And Mapping ◽  
Outdoor Environments ◽  
Stable Feature

In this paper, we propose a set of techniques for accurate and practical Simultaneous Localization And Mapping (SLAM) in dynamic outdoor environments. The techniques are categorized into Landmark estimation and Unscented particle sampling. Landmark estimation features stable feature detection and data management for estimating landmarks accurately, robustly, and at a low-calculation cost. The stable feature detection removes dynamic objects and sensor noise with scan subtraction, detects feature points sparsely and evenly, and sets data association parameters with landmark density. The data management calculates landmark existence probability and spurious landmarks are removed, utilizes landmark exclusivity for data association, and predicts importance weights using the observation range. Unscented particle sampling is based on Unscented Transformation for accurate SLAM. Simulation results of SLAM using our landmark estimation and experimental results of our SLAM in dynamic outdoor environments are presented and discussed. The results show that our landmark estimation decrease SLAM calculation time and maximum position error by 80% compared to conventional landmark estimation, and position estimation of SLAM with Unscented particle sampling ismore accurate than FastSLAM2.0 in dynamic outdoor environments.

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