scholarly journals SVG-Loop: Semantic–Visual–Geometric Information-Based Loop Closure Detection

2021 ◽  
Vol 13 (17) ◽  
pp. 3520
Author(s):  
Zhian Yuan ◽  
Ke Xu ◽  
Xiaoyu Zhou ◽  
Bin Deng ◽  
Yanxin Ma
Keyword(s):  
Detection Algorithm ◽  
Detection Methods ◽  
Vector Model ◽  
Limited Information ◽  
Complex Environments ◽  
Dynamic Features ◽  
Geometric Information ◽  
Loop Closure ◽  
Loop Closure Detection ◽  
Localization And Mapping

Loop closure detection is an important component of visual simultaneous localization and mapping (SLAM). However, most existing loop closure detection methods are vulnerable to complex environments and use limited information from images. As higher-level image information and multi-information fusion can improve the robustness of place recognition, a semantic–visual–geometric information-based loop closure detection algorithm (SVG-Loop) is proposed in this paper. In detail, to reduce the interference of dynamic features, a semantic bag-of-words model was firstly constructed by connecting visual features with semantic labels. Secondly, in order to improve detection robustness in different scenes, a semantic landmark vector model was designed by encoding the geometric relationship of the semantic graph. Finally, semantic, visual, and geometric information was integrated by fuse calculation of the two modules. Compared with art-of-the-state methods, experiments on the TUM RBG-D dataset, KITTI odometry dataset, and practical environment show that SVG-Loop has advantages in complex environments with varying light, changeable weather, and dynamic interference.

scholarly journals A BigBiGAN-Based Loop Closure Detection Algorithm for Indoor Visual SLAM

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Qiubo Zhong ◽  
Xiaoyi Fang
Keyword(s):  
Unsupervised Learning ◽  
Recall Rate ◽  
Detection Algorithm ◽  
Detection Methods ◽  
Bag Of Words ◽  
Learning Method ◽  
Visual Slam ◽  
Loop Closure ◽  
Loop Closure Detection ◽  
Main Component

Loop closure detection serves as the fulcrum of improving the accuracy and precision in simultaneous localization and mapping (SLAM). The majority of loop detection methods extract artificial features, which fall short of learning comprehensive data information, but unsupervised learning as a typical deep learning method excels in self-access learning and clustering to analyze the similarity without handling the data. Moreover, the unsupervised learning method does solve restrictions on image quality and singleness semantics in many traditional SLAM methods. Therefore, a loop closure detection strategy based on an unsupervised learning method is proposed in this paper. The main component adopts BigBiGAN to extract features and establish an original bag of words. Then, the complete bag of words is used to detect loop closing. Finally, a considerable validation check of the ORB descriptor is added to verify the result and output outcome of loop closure detection. The proposed algorithm and other compared algorithms are, respectively, applied on Autolabor Pro1 to execute the indoor visual SLAM. The experiment shows that the proposed algorithm increases the recall rate by 20% compared with ORB-SLAM2 and LSD-SLAM. And it also improves at least 40.0% accuracy than others and reduces 14% time loss of ORB-SLAM2. Therefore, the presented SLAM based on BigBiGAN does benefit much the visual SLAM in the indoor environment.

scholarly journals Loop Closure Detection in RGB-D SLAM by Utilizing Siamese ConvNet Features

2021 ◽  
Vol 12 (1) ◽  
pp. 62
Author(s):  
Gang Xu ◽  
Xiang Li ◽  
Xingyu Zhang ◽  
Guangxin Xing ◽  
Feng Pan
Keyword(s):  
Detection Methods ◽  
Position Information ◽  
Loop Closure ◽  
Loop Closure Detection ◽  
Depth Images ◽  
Localization And Mapping ◽  
L2 Norm ◽  
Significant Research ◽  
Key Issues ◽  
Public Datasets

Loop closure detection is a key challenge in visual simultaneous localization and mapping (SLAM) systems, which has attracted significant research interest in recent years. It entails correctly determining whether a scene has previously been visited by a mobile robot and completely establishing the consistent maps of motion. There are many loop closure detection methods that have been proposed, but most of these algorithms are handcrafted features-based and perform weak robustness to illumination variations. In this paper, we investigate a Siamese Convolutional Neural Network (SCNN) to solve the task of loop closure detection in RGB-D SLAM. Firstly, we use a pre-trained SCNN model to extract features as image descriptors; then, the L2 norm distance is adopted as a similarity metric between descriptors. In terms of the learned features for matching, there are two key issues for discussion: (1) how to define an appropriate loss as supervision (utilizing the cross-entropy loss, the contrastive loss, or the combination of two); and (2) how to combine the appearance information in RGB images and position information in depth images (utilizing early fusion, mid-level fusion or late fusion). We compare our proposed method of different baseline by experiments carried out on two public datasets (New College and NYU), and our performance outperforms the state-of-the-art.

Loop Closure Detection Algorithm Based On Multi-Level Convolutional Neural Network Features

2018 ◽  
Vol 55 (11) ◽  
pp. 111507
Author(s):  
鲍振强 Bao Zhenqiang ◽  
李艾华 Li Aihua ◽  
崔智高 Cui Zhigao ◽  
苏延召 Su Yanzhao ◽  
郑勇 Zheng Yong
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Detection Algorithm ◽  
Loop Closure ◽  
Loop Closure Detection ◽  
Multi Level

scholarly journals Incremental Loop Closure Verification by Guided Sampling

2017 ◽  
Vol 21 (1) ◽  
pp. 59-66
Author(s):  
Tanaka Kanji ◽  
Keyword(s):  
Image Retrieval ◽  
Good Precision ◽  
Sampling Strategies ◽  
Detection Problem ◽  
Loop Closure ◽  
Loop Closure Detection ◽  
Robot Trajectory ◽  
Localization And Mapping ◽  
Random Sample Consensus ◽  
Closure Constraints

Loop closure detection, which is the task of identifying locations revisited by a robot in a sequence of odometry and perceptual observations, is typically formulated as a combination of two subtasks: (1) bag-of-words image retrieval and (2) post-verification using random sample consensus (RANSAC) geometric verification. The main contribution of this study is the proposal of a novel post-verification framework that achieves good precision recall trade-off in loop closure detection. This study is motivated by the fact that not all loop closure hypotheses are equally plausible (e.g., owing to mutual consistency between loop closure constraints) and that if we have evidence that one hypothesis is more plausible than the others, then it should be verified more frequently. We demonstrate that the loop closure detection problem can be viewed as an instance of a multi-model hypothesize-and-verify framework. Thus, we can build guided sampling strategies on this framework where loop closures proposed using image retrieval are verified in a planned order (rather than in a conventional uniform order) to operate in a constant time. Experimental results using a stereo simultaneous localization and mapping (SLAM) system confirm that the proposed strategy, the use of loop closure constraints and robot trajectory hypotheses as a guide, achieves promising results despite the fact that there exists a significant number of false positive constraints and hypotheses.

scholarly journals Multi-Objective Optimization of Loop Closure Detection Parameters for Indoor 2D Simultaneous Localization and Mapping

Sensors ◽  
2020 ◽  
Vol 20 (7) ◽  
pp. 1906
Author(s):  
Dongxiao Han ◽  
Yuwen Li ◽  
Tao Song ◽  
Zhenyang Liu
Keyword(s):  
Simultaneous Localization And Mapping ◽  
Ground Truth ◽  
Optimization Method ◽  
Evaluation Metrics ◽  
Multi Objective Optimization ◽  
Loop Closure ◽  
Loop Closure Detection ◽  
Multi Objective ◽  
Evaluation Approach ◽  
Localization And Mapping

Aiming at addressing the issues related to the tuning of loop closure detection parameters for indoor 2D graph-based simultaneous localization and mapping (SLAM), this article proposes a multi-objective optimization method for these parameters. The proposed method unifies the Karto SLAM algorithm, an efficient evaluation approach for map quality with three quantitative metrics, and a multi-objective optimization algorithm. More particularly, the evaluation metrics, i.e., the proportion of occupied grids, the number of corners and the amount of enclosed areas, can reflect the errors such as overlaps, blurring and misalignment when mapping nested loops, even in the absence of ground truth. The proposed method has been implemented and validated by testing on four datasets and two real-world environments. For all these tests, the map quality can be improved using the proposed method. Only loop closure detection parameters have been considered in this article, but the proposed evaluation metrics and optimization method have potential applications in the automatic tuning of other SLAM parameters to improve the map quality.

scholarly journals A Light Visual Mapping and Navigation Framework for Low-Cost Robots

2015 ◽  
Vol 24 (4) ◽  
pp. 505-524 ◽  
Author(s):  
Stephane Bazeille ◽  
Emmanuel Battesti ◽  
David Filliat
Keyword(s):  
Large Range ◽  
Low Cost ◽  
Indoor Environments ◽  
Noise Levels ◽  
Visual Sensor ◽  
Loop Closure ◽  
Loop Closure Detection ◽  
Localization And Mapping ◽  
Computational Resources

AbstractWe address the problems of localization, mapping, and guidance for robots with limited computational resources by combining vision with the metrical information given by the robot odometry. We propose in this article a novel light and robust topometric simultaneous localization and mapping framework using appearance-based visual loop-closure detection enhanced with the odometry. The main advantage of this combination is that the odometry makes the loop-closure detection more accurate and reactive, while the loop-closure detection enables the long-term use of odometry for guidance by correcting the drift. The guidance approach is based on qualitative localization using vision and odometry, and is robust to visual sensor occlusions or changes in the scene. The resulting framework is incremental, real-time, and based on cheap sensors provided on many robots (a camera and odometry encoders). This approach is, moreover, particularly well suited for low-power robots as it is not dependent on the image processing frequency and latency, and thus it can be applied using remote processing. The algorithm has been validated on a Pioneer P3DX mobile robot in indoor environments, and its robustness is demonstrated experimentally for a large range of odometry noise levels.

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.

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