Simultaneous Localization and Mapping in the Epoch of Semantics: A Survey

Sualeh, Muhammad; Kim, Gon–Woo

doi:10.1007/s12555-018-0130-x

Cited by 64 publications

(30 citation statements)

References 53 publications

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“…Comparisons between the state-of-the-art solutions back in 2016 were conducted, followed by a set of open research problems relating to the mentioned categories. Finally, a recent survey on SLAM, with focus on semantics can be found in [115]. In this paper, we contribute a comprehensive survey of the most recent state-of-the-art feature-based visual SLAM systems and we classify the reviewed approaches based on the elements, i.e.…”

Section: Existing Surveys On Slammentioning

confidence: 99%

Feature-based visual simultaneous localization and mapping: a survey

Azzam

Taha²,

Huang

et al. 2020

SN Appl. Sci.

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Visual simultaneous localization and mapping (SLAM) has attracted high attention over the past few years. In this paper, a comprehensive survey of the state-of-the-art feature-based visual SLAM approaches is presented. The reviewed approaches are classified based on the visual features observed in the environment. Visual features can be seen at different levels; low-level features like points and edges, middle-level features like planes and blobs, and high-level features like semantically labeled objects. One of the most critical research gaps regarding visual SLAM approaches concluded from this study is the lack of generality. Some approaches exhibit a very high level of maturity, in terms of accuracy and efficiency. Yet, they are tailored to very specific environments, like feature-rich and static environments. When operating in different environments, such approaches experience severe degradation in performance. In addition, due to software and hardware limitations, guaranteeing a robust visual SLAM approach is extremely challenging. Although semantics have been heavily exploited in visual SLAM, understanding of the scene by incorporating relationships between features is not yet fully explored. A detailed discussion of such research challenges is provided throughout the paper.

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Section: Existing Surveys On Slammentioning

confidence: 99%

Feature-based visual simultaneous localization and mapping: a survey

Azzam

Taha²,

Huang

et al. 2020

SN Appl. Sci.

View full text Add to dashboard Cite

show abstract

“…Over the years, the researches of performance comparison of methods that have emerged before significantly increased as well [32]. Sualeh et al [33] recently have projected into future potential SLAM studies as well as scanning past studies [34]. Huang [35] screened and analyzed the studies of SLAM (optimization) strategies from the outset, Yavuz et al [36] compared EKF, UKF, and Fast SLAM performances.…”

Section: Literature Reviewmentioning

confidence: 99%

Exact flow of particles using for state estimations in unmanned aerial systems` navigation

2020

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The navigation is a substantial issue in the field of robotics. Simultaneous Localization and Mapping (SLAM) is a principle for many autonomous navigation applications, particularly in the Global Navigation Satellite System (GNSS) denied environments. Many SLAM methods made substantial contributions to improve its accuracy, cost, and efficiency. Still, it is a considerable challenge to manage robust SLAM, and there exist several attempts to find better estimation algorithms for it. In this research, we proposed a novel Bayesian filtering based Airborne SLAM structure for the first time in the literature. We also presented the mathematical background of the algorithm, and the SLAM model of an autonomous aerial vehicle. Simulation results emphasize that the new Airborne SLAM performance with the exact flow of particles using for recursive state estimations superior to other approaches emerged before, in terms of accuracy and speed of convergence. Nevertheless, its computational complexity may cause real-time application concerns, particularly in high-dimensional state spaces. However, in Airborne SLAM, it can be preferred in the measurement environments that use low uncertainty sensors because it gives more successful results by eliminating the problem of degeneration seen in the particle filter structure.

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“…To this end, the adaptive and generic corner detection via the accelerated segment test (AGAST) (Mair, Hager, Burschka, Suppa, & Hirzinger, 2010;Sualeh & Kim, 2019) is first developed to improve time efficiency in the processing of feature point extraction. Instead of Brute Force (BF) match, the multi-probe LSH (Lv, Josephson, Wang, Charikar, & Li, 2007), with for improving time efficiency, is adopted to approximate linear search the feature point set to get rough matching.…”

Section: Introductionmentioning

confidence: 99%

Improved feature point extraction and mismatch eliminating algorithm

Sun

Zhang

Wang

2020

Systems Science & Control Engineering

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For the existing image matching algorithms, some inherent shortcomings, such as high mismatch rate and low computational efficiency, give rise to a bad influence on the performance of Visual Simultaneous Localization and Mapping (VSLAM). In this paper, a Grid-Based Motion Statistics for Fast and Random Sample Consensus (GMS-RANSAC) method combining with Multi-Probe Location Sensitive Hash (LSH)-based Adaptive and Generic Corner Detection Based on the Accelerated Segment Test and Oriented FAST and Rotated BRIEF (AGAST-ORB) algorithm is proposed to improve the real time and accuracy of image matching. To this end, the AGAST algorithm and the multi-probe LSH algorithm are firstly integrated into the traditional ORB algorithm to obtain the initial matching set. Specifically, the image feature points are extracted by the AGAST algorithm and then the main direction of feature points is given according to the intensity centroid method to guarantee the rotary invariant of feature points. Based on the extracted feature points, the multi-probe LSH algorithm, benefiting from its high time efficiency, is used to generate the initial matching pairs. In what follows, a GMS-RANSAC algorithm, which is improved by adding a directional similarity constraint model and the traditional RANSAC algorithm, is performed to improve the accuracy of eliminating result further. Finally, the performance test is implemented via a Mikolajczyk standard data set and it is verified that the proposed algorithm has higher matching precision and matching efficiency than traditional image matching algorithms.

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Simultaneous Localization and Mapping in the Epoch of Semantics: A Survey

Cited by 64 publications

References 53 publications

Feature-based visual simultaneous localization and mapping: a survey

Feature-based visual simultaneous localization and mapping: a survey

Exact flow of particles using for state estimations in unmanned aerial systems` navigation

Improved feature point extraction and mismatch eliminating algorithm

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