scholarly journals Reliable Visual Exploration System with Fault Tolerance Structure

2019 ◽  
Vol 9 (4) ◽  
pp. 662
Author(s):  
Weinan Chen ◽  
Lei Zhu ◽  
Li He ◽  
Yisheng Guan ◽  
Hong Zhang
Keyword(s):  
Fault Tolerance ◽  
Visual Tracking ◽  
Evaluation Method ◽  
Semantic Segmentation ◽  
Visual Exploration ◽  
Visual Localization ◽  
Challenging Problem ◽  
Visual Mapping ◽  
Localization And Mapping ◽  
Failure Conditions

Reliability of visual tracking and mapping is a challenging problem in robotics research, and it limits the promotion of vision-based mobile robot applications to a great extent. In this paper, we propose to improve the reliability of visual exploration in terms of its fault tolerance. Three modules are involved in our visual exploration system: visual localization and mapping, active controller and termination condition. High maintainability of mapping is obtained by the submap-based visual mapping module, persistent driving is achieved by a semantic segmentation based active controller, and robustness of re-localization is guaranteed by a novel completeness evaluation method in the termination condition. All the modules are integrated tightly for maintaining mapping and improving visual tracking. The system is verified with simulations and real world experiments, and all the solutions to fault tolerance are verified to overcome the failure conditions of visual tracking and mapping.

scholarly journals A Visual SLAM Robust against Dynamic Objects Based on Hybrid Semantic-Geometry Information

2021 ◽  
Vol 10 (10) ◽  
pp. 673
Author(s):  
Sheng Miao ◽  
Xiaoxiong Liu ◽  
Dazheng Wei ◽  
Changze Li
Keyword(s):  
Moving Objects ◽  
State Of The Art ◽  
Semantic Segmentation ◽  
Visual Localization ◽  
Epipolar Constraint ◽  
Trajectory Error ◽  
Localization And Mapping ◽  
Dynamic Objects ◽  
Public Datasets ◽  
Localization Approach

A visual localization approach for dynamic objects based on hybrid semantic-geometry information is presented. Due to the interference of moving objects in the real environment, the traditional simultaneous localization and mapping (SLAM) system can be corrupted. To address this problem, we propose a method for static/dynamic image segmentation that leverages semantic and geometric modules, including optical flow residual clustering, epipolar constraint checks, semantic segmentation, and outlier elimination. We integrated the proposed approach into the state-of-the-art ORB-SLAM2 and evaluated its performance on both public datasets and a quadcopter platform. Experimental results demonstrated that the root-mean-square error of the absolute trajectory error improved, on average, by 93.63% in highly dynamic benchmarks when compared with ORB-SLAM2. Thus, the proposed method can improve the performance of state-of-the-art SLAM systems in challenging scenarios.

Localization and Mapping for Indoor Navigation

2020 ◽  
pp. 930-954 ◽  
Author(s):  
Heba Gaber ◽  
Mohamed Marey ◽  
Safaa Amin ◽  
Mohamed F. Tolba
Keyword(s):  
System Architecture ◽  
Large Range ◽  
Simultaneous Localization And Mapping ◽  
Indoor Navigation ◽  
Indoor Environments ◽  
Challenging Problem ◽  
Unknown Environments ◽  
Localization And Mapping ◽  
Robotic Missions

Mapping and exploration for the purpose of navigation in unknown or partially unknown environments is a challenging problem, especially in indoor environments where GPS signals can't give the required accuracy. This chapter discusses the main aspects for designing a Simultaneous Localization and Mapping (SLAM) system architecture with the ability to function in situations where map information or current positions are initially unknown or partially unknown and where environment modifications are possible. Achieving this capability makes these systems significantly more autonomous and ideal for a large range of applications, especially indoor navigation for humans and for robotic missions. This chapter surveys the existing algorithms and technologies used for localization and mapping and highlights on using SLAM algorithms for indoor navigation. Also the proposed approach for the current research is presented.

scholarly journals Application of a Real-Time Visualization Method of AUVs in Underwater Visual Localization

2019 ◽  
Vol 9 (7) ◽  
pp. 1428 ◽  
Author(s):  
Ran Wang ◽  
Xin Wang ◽  
MingMing Zhu ◽  
YinFu Lin
Keyword(s):  
Autonomous Underwater Vehicles ◽  
Underwater Vehicles ◽  
Visual Localization ◽  
Visualization Method ◽  
Localization And Mapping ◽  
Real Time Visualization ◽  
Novel Method ◽  
Vision Based Localization ◽  
Slam Algorithm ◽  
Localization Systems

Autonomous underwater vehicles (AUVs) are widely used, but it is a tough challenge to guarantee the underwater location accuracy of AUVs. In this paper, a novel method is proposed to improve the accuracy of vision-based localization systems in feature-poor underwater environments. The traditional stereo visual simultaneous localization and mapping (SLAM) algorithm, which relies on the detection of tracking features, is used to estimate the position of the camera and establish a map of the environment. However, it is hard to find enough reliable point features in underwater environments and thus the performance of the algorithm is reduced. A stereo point and line SLAM (PL-SLAM) algorithm for localization, which utilizes point and line information simultaneously, was investigated in this study to resolve the problem. Experiments with an AR-marker (Augmented Reality-marker) were carried out to validate the accuracy and effect of the investigated algorithm.

Improving Registration of Augmented Reality by Incorporating DCNNS into Visual SLAM

2018 ◽  
Vol 32 (12) ◽  
pp. 1855022 ◽  
Author(s):  
Yongbin Chen ◽  
Hanwu He ◽  
Heen Chen ◽  
Teng Zhu
Keyword(s):  
Augmented Reality ◽  
Three Dimensional ◽  
Semantic Segmentation ◽  
Recognition Algorithm ◽  
Scene Recognition ◽  
3D Registration ◽  
Deep Convolutional Neural Networks ◽  
Severe Problem ◽  
Localization And Mapping ◽  
Semantic Scene

Augmented reality (AR) by analyzing the characteristics of the scene, the computer-generated geometric information which can be added to the real environment in the way of visual fusion, reinforces the perception of the world. Three-dimensional (3D) registration is one of the core issues of in AR. The key issue is to estimate the visual sensor’s posture in the 3D environment and figure out the objects in the scene. Recently, computer vision has made significant progress, but the registration based on natural feature points in 3D space for AR system is still a severe problem. There is the difficulty of working out the mobile camera’s posture in the 3D scene precisely due to the unstable factors, such as the image noise, changing light and the complex background pattern. Therefore, to design a stable, reliable and efficient scene recognition algorithm is still very challenging work. In this paper, we propose an algorithm which combines Visual Simultaneous Localization and Mapping (SLAM) and Deep Convolutional Neural Networks (DCNNS) to boost the performance of AR registration. Semantic segmentation is a dense prediction task which aims to predict categories for each pixel in an image when applying to AR registration, and it will be able to narrow the searching range of the feature point between the two frames thus enhancing the stability of the system. Comparative experiments in this paper show that the semantic scene information will bring a revolutionary breakthrough to the AR interaction.

scholarly journals Weakly Supervised and Semi-Supervised Semantic Segmentation for Optic Disc of Fundus Image

Symmetry ◽  
2020 ◽  
Vol 12 (1) ◽  
pp. 145 ◽  
Author(s):  
Zheng Lu ◽  
Dali Chen
Keyword(s):  
Computer Vision ◽  
Image Segmentation ◽  
Medical Image ◽  
Semantic Segmentation ◽  
Medical Image Segmentation ◽  
Fundus Image ◽  
Challenging Problem ◽  
Weakly Supervised ◽  
Fine Tune ◽  
State Of Art

Weakly supervised and semi-supervised semantic segmentation has been widely used in the field of computer vision. Since it does not require groundtruth or it only needs a small number of groundtruths for training. Recently, some works use pseudo groundtruths which are generated by a classified network to train the model, however, this method is not suitable for medical image segmentation. To tackle this challenging problem, we use the GrabCut method to generate the pseudo groundtruths in this paper, and then we train the network based on a modified U-net model with the generated pseudo groundtruths, finally we utilize a small amount of groundtruths to fine tune the model. Extensive experiments on the challenging RIM-ONE and DRISHTI-GS benchmarks strongly demonstrate the effectiveness of our algorithm. We obtain state-of-art results on RIM-ONE and DRISHTI-GS databases.

scholarly journals Application of Multilayer Neural Network in Sports Psychology

2022 ◽  
Vol 2022 ◽  
pp. 1-7
Author(s):  
Hongling Yang
Keyword(s):  
Neural Network ◽  
Fault Tolerance ◽  
Evaluation Method ◽  
Nonlinear Problems ◽  
Sports Psychology ◽  
Fault Analysis ◽  
Network Control ◽  
Psychological Characteristics ◽  
Main Task ◽  
Sports Science

The research on multilayer neural network theory has developed rapidly in recent years. It has parallel processing capabilities and fault tolerance and has aroused the interest of many researchers. The neural network has made great progress in the field of control, especially in model identification and control. It has been quickly applied in the fields of device design, optimized operation, and fault analysis and diagnosis. Neural network control, as an automated control technology in the 21st century, has been fully proved by theories and practices at home and abroad, and it is very useful in complex process control. Sports psychology is a discipline that studies the psychological characteristics and laws of people engaged in sports, and it is also a new development in sports science. The main task of sports psychology is to study people’s psychological processes when participating in sports, such as feeling, perception, appearance, thinking, memory, emotion, and characteristics of will and its role and significance in sports. An important feature of multilayer neural networks is to achieve results that match the expected output through network learning. It has strong self-learning, self-adaptability, and fault tolerance. The multilayer neural network system evaluation method is unique with its extraordinary ability to deal with complex nonlinear problems and does not involve human intervention. This article presents a multilayer neural network algorithm, which classifies the samples of athletes, and studies the physical education training process, the psychological characteristics of related personnel in sports competitions, such as the psychological characteristics of the formation of sports skills, and the psychological training of athletes before the game.

Sign in / Sign up

Export Citation Format

Share Document