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 A resource-aware approach to collaborative loop-closure detection with provable performance guarantees

2020 ◽  
pp. 027836492094859
Author(s):  
Yulun Tian ◽  
Kasra Khosoussi ◽  
Jonathan P How
Keyword(s):  
Resource Constraints ◽  
Low Cost ◽  
A Priori ◽  
Convex Relaxation ◽  
Loop Closure ◽  
Loop Closure Detection ◽  
Performance Guarantees ◽  
Localization And Mapping ◽  
Resource Aware ◽  
Geometric Verification

This paper presents resource-aware algorithms for distributed inter-robot loop-closure detection for applications such as collaborative simultaneous localization and mapping (CSLAM) and distributed image retrieval. In real-world scenarios, this process is resource-intensive as it involves exchanging many observations and geometrically verifying a large number of potential matches. This poses severe challenges for small-size and low-cost robots with various operational and resource constraints that limit, e.g., energy consumption, communication bandwidth, and computation capacity. This paper proposes a framework in which robots first exchange compact queries to identify a set of potential loop closures. We then seek to select a subset of potential inter-robot loop closures for geometric verification that maximizes a monotone submodular performance metric without exceeding budgets on computation (number of geometric verifications) and communication (amount of exchanged data for geometric verification). We demonstrate that this problem is, in general, NP-hard, and present efficient approximation algorithms with provable a priori performance guarantees. The proposed framework is extensively evaluated on real and synthetic datasets. A natural convex relaxation scheme is also presented to certify the near-optimal performance of the proposed framework a posteriori.

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.

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.

Sequence-based sparse optimization methods for long-term loop closure detection in visual SLAM

2018 ◽  
Vol 42 (7) ◽  
pp. 1323-1335 ◽  
Author(s):  
Fei Han ◽  
Hua Wang ◽  
Guoquan Huang ◽  
Hao Zhang
Keyword(s):  
Optimization Methods ◽  
Sparse Optimization ◽  
Visual Slam ◽  
Loop Closure ◽  
Loop Closure Detection

scholarly journals Long-Term Loop Closure Detection through Visual-Spatial Information Preserving Multi-Order Graph Matching

2020 ◽  
Vol 34 (06) ◽  
pp. 10369-10376
Author(s):  
Peng Gao ◽  
Hao Zhang
Keyword(s):  
Spatial Information ◽  
Graph Matching ◽  
Fundamental Problem ◽  
Graph Representation ◽  
Spatial Relationships ◽  
Matching Problem ◽  
Loop Closure ◽  
Loop Closure Detection ◽  
Visual Spatial

Loop closure detection is a fundamental problem for simultaneous localization and mapping (SLAM) in robotics. Most of the previous methods only consider one type of information, based on either visual appearances or spatial relationships of landmarks. In this paper, we introduce a novel visual-spatial information preserving multi-order graph matching approach for long-term loop closure detection. Our approach constructs a graph representation of a place from an input image to integrate visual-spatial information, including visual appearances of the landmarks and the background environment, as well as the second and third-order spatial relationships between two and three landmarks, respectively. Furthermore, we introduce a new formulation that formulates loop closure detection as a multi-order graph matching problem to compute a similarity score directly from the graph representations of the query and template images, instead of performing conventional vector-based image matching. We evaluate the proposed multi-order graph matching approach based on two public long-term loop closure detection benchmark datasets, including the St. Lucia and CMU-VL datasets. Experimental results have shown that our approach is effective for long-term loop closure detection and it outperforms the previous state-of-the-art methods.

scholarly journals A Novel Loop Closure Detection Approach Using Simplified Structure for Low-Cost LiDAR

Sensors ◽  
2020 ◽  
Vol 20 (8) ◽  
pp. 2299
Author(s):  
Qin Ye ◽  
Pengcheng Shi ◽  
Kunyuan Xu ◽  
Popo Gui ◽  
Shaoming Zhang
Keyword(s):  
Point Cloud ◽  
Detection Efficiency ◽  
Low Cost ◽  
Evaluation Model ◽  
Level Structure ◽  
Geometric Constraints ◽  
Global Localization ◽  
Point Cloud Registration ◽  
Loop Closure ◽  
Loop Closure Detection

Reducing the cumulative error is a crucial task in simultaneous localization and mapping (SLAM). Usually, Loop Closure Detection (LCD) is exploited to accomplish this work for SLAM and robot navigation. With a fast and accurate loop detection, it can significantly improve global localization stability and reduce mapping errors. However, the LCD task based on point cloud still has some problems, such as over-reliance on high-resolution sensors, and poor detection efficiency and accuracy. Therefore, in this paper, we propose a novel and fast global LCD method using a low-cost 16 beam Lidar based on “Simplified Structure”. Firstly, we extract the “Simplified Structure” from the indoor point cloud, classify them into two levels, and manage the “Simplified Structure” hierarchically according to its structure salience. The “Simplified Structure” has simple feature geometry and can be exploited to capture the indoor stable structures. Secondly, we analyze the point cloud registration suitability with a pre-match, and present a hierarchical matching strategy with multiple geometric constraints in Euclidean Space to match two scans. Finally, we construct a multi-state loop evaluation model for a multi-level structure to determine whether the two candidate scans are a loop. In fact, our method also provides a transformation for point cloud registration with “Simplified Structure” when a loop is detected successfully. Experiments are carried out on three types of indoor environment. A 16 beam Lidar is used to collect data. The experimental results demonstrate that our method can detect global loop closures efficiently and accurately. The average global LCD precision, accuracy and negative are approximately 0.90, 0.96, and 0.97, respectively.

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 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 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 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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