Loop Closure Detection for Monocular Visual Odometry: Deep-Learning Approaches Comparison

2019 ◽  
Author(s):  
Mohamed Ali Sedrine ◽  
Wided Souidene Mseddi ◽  
Takoua Abdellatif ◽  
Rabah Attia
Keyword(s):  
Deep Learning ◽  
Visual Odometry ◽  
Learning Approaches ◽  
Loop Closure ◽  
Loop Closure Detection

scholarly journals Dynamic-DSO: Direct Sparse Odometry Using Objects Semantic Information for Dynamic Environments

2020 ◽  
Vol 10 (4) ◽  
pp. 1467
Author(s):  
Chao Sheng ◽  
Shuguo Pan ◽  
Wang Gao ◽  
Yong Tan ◽  
Tao Zhao
Keyword(s):  
Semantic Information ◽  
Direct Method ◽  
Visual Odometry ◽  
Dynamic Environments ◽  
Loop Closure ◽  
Loop Closure Detection ◽  
Camera Pose ◽  
Photometric Error ◽  
Tracking Model ◽  
Dynamic Objects

Traditional Simultaneous Localization and Mapping (SLAM) (with loop closure detection), or Visual Odometry (VO) (without loop closure detection), are based on the static environment assumption. When working in dynamic environments, they perform poorly whether using direct methods or indirect methods (feature points methods). In this paper, Dynamic-DSO which is a semantic monocular direct visual odometry based on DSO (Direct Sparse Odometry) is proposed. The proposed system is completely implemented with the direct method, which is different from the most current dynamic systems combining the indirect method with deep learning. Firstly, convolutional neural networks (CNNs) are applied to the original RGB image to generate the pixel-wise semantic information of dynamic objects. Then, based on the semantic information of the dynamic objects, dynamic candidate points are filtered out in keyframes candidate points extraction; only static candidate points are reserved in the tracking and optimization module, to achieve accurate camera pose estimation in dynamic environments. The photometric error calculated by the projection points in dynamic region of subsequent frames are removed from the whole photometric error in pyramid motion tracking model. Finally, the sliding window optimization which neglects the photometric error calculated in the dynamic region of each keyframe is applied to obtain the precise camera pose. Experiments on the public TUM dynamic dataset and the modified Euroc dataset show that the positioning accuracy and robustness of the proposed Dynamic-DSO is significantly higher than the state-of-the-art direct method in dynamic environments, and the semi-dense cloud map constructed by Dynamic-DSO is clearer and more detailed.

scholarly journals Role of Deep Learning in Loop Closure Detection for Visual and Lidar SLAM: A Survey

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1243
Author(s):  
Saba Arshad ◽  
Gon-Woo Kim
Keyword(s):  
Deep Learning ◽  
Detailed Comparison ◽  
Loop Closure ◽  
Loop Closure Detection ◽  
Detection Algorithms ◽  
Mobile Objects ◽  
Localization And Mapping ◽  
Loop Detection ◽  
Scene Representation ◽  
Matching Strategy

Loop closure detection is of vital importance in the process of simultaneous localization and mapping (SLAM), as it helps to reduce the cumulative error of the robot’s estimated pose and generate a consistent global map. Many variations of this problem have been considered in the past and the existing methods differ in the acquisition approach of query and reference views, the choice of scene representation, and associated matching strategy. Contributions of this survey are many-fold. It provides a thorough study of existing literature on loop closure detection algorithms for visual and Lidar SLAM and discusses their insight along with their limitations. It presents a taxonomy of state-of-the-art deep learning-based loop detection algorithms with detailed comparison metrics. Also, the major challenges of conventional approaches are identified. Based on those challenges, deep learning-based methods were reviewed where the identified challenges are tackled focusing on the methods providing long-term autonomy in various conditions such as changing weather, light, seasons, viewpoint, and occlusion due to the presence of mobile objects. Furthermore, open challenges and future directions were also discussed.

scholarly journals Leveraging Deep Learning for Visual Odometry Using Optical Flow

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1313
Author(s):  
Tejas Pandey ◽  
Dexmont Pena ◽  
Jonathan Byrne ◽  
David Moloney
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Optical Flow ◽  
Recurrent Neural Networks ◽  
Deep Neural Networks ◽  
Visual Odometry ◽  
Camera Motion ◽  
Learning Approaches ◽  
Outlier Rejection ◽  
Entire Trajectory

In this paper, we study deep learning approaches for monocular visual odometry (VO). Deep learning solutions have shown to be effective in VO applications, replacing the need for highly engineered steps, such as feature extraction and outlier rejection in a traditional pipeline. We propose a new architecture combining ego-motion estimation and sequence-based learning using deep neural networks. We estimate camera motion from optical flow using Convolutional Neural Networks (CNNs) and model the motion dynamics using Recurrent Neural Networks (RNNs). The network outputs the relative 6-DOF camera poses for a sequence, and implicitly learns the absolute scale without the need for camera intrinsics. The entire trajectory is then integrated without any post-calibration. We evaluate the proposed method on the KITTI dataset and compare it with traditional and other deep learning approaches in the literature.

Deep Learning Approaches for Whiteboard Image Quality Enhancement

2019 ◽  
Vol 2019 (1) ◽  
pp. 360-368
Author(s):  
Mekides Assefa Abebe ◽  
Jon Yngve Hardeberg
Keyword(s):  
Deep Learning ◽  
Image Quality ◽  
Image Data ◽  
Quality Enhancement ◽  
Network Architectures ◽  
Learning Approaches ◽  
Data Set ◽  
Image Quality Enhancement ◽  
Processing Techniques ◽  
White Balancing

Different whiteboard image degradations highly reduce the legibility of pen-stroke content as well as the overall quality of the images. Consequently, different researchers addressed the problem through different image enhancement techniques. Most of the state-of-the-art approaches applied common image processing techniques such as background foreground segmentation, text extraction, contrast and color enhancements and white balancing. However, such types of conventional enhancement methods are incapable of recovering severely degraded pen-stroke contents and produce artifacts in the presence of complex pen-stroke illustrations. In order to surmount such problems, the authors have proposed a deep learning based solution. They have contributed a new whiteboard image data set and adopted two deep convolutional neural network architectures for whiteboard image quality enhancement applications. Their different evaluations of the trained models demonstrated their superior performances over the conventional methods.

Assessment of the Risk Factors of MDD Recurrence Based on Deep Learning Approaches

2019 ◽  
Author(s):  
Qian Wu ◽  
Weiling Zhao ◽  
Xiaobo Yang ◽  
Hua Tan ◽  
Lei You ◽  
...  
Keyword(s):  
Risk Factors ◽  
Deep Learning ◽  
Learning Approaches
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