Probabilistic Appearance-Based Place Recognition Through Bag of Tracked Words

Tsintotas, Konstantinos A.; Bampis, Loukas; Γαστεράτος, Αντώνιος

doi:10.1109/lra.2019.2897151

Cited by 50 publications

(28 citation statements)

References 31 publications

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“…These descriptors are usually very fast to compute, though, typically, they are more sensitive to viewpoint and illumination changes than local features. Due to these reasons, point local descriptors, either real-valued (Cummins and Newman 2008;Angeli et al 2008;Cummins and Newman 2011;Tsintotas et al 2019) or binary (Galvez-López and Tardos 2012; Mur-Artal and Tardós 2014; Khan and Wollherr 2015;Garcia-Fidalgo and Ortiz 2018), have been widely used in the literature during last decades. More recently, approaches based on CNNs have emerged as an alternative, motivated by their demonstrated robustness to visual appearance changes (Chen et al 2014;Sünderhauf et al 2015;Chen et al 2017;Kenshimov et al 2017;Lopez-Antequera et al 2017;Yue et al 2019).…”

Section: Related Workmentioning

confidence: 99%

Appearance-based loop closure detection combining lines and learned points for low-textured environments

2022

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Hand-crafted point descriptors have been traditionally used for visual loop closure detection. However, in low-textured environments, it is usually difficult to find enough point features and, hence, the performance of such algorithms degrade. Under this context, this paper proposes a loop closure detection method that combines lines and learned points to work, particularly, in scenarios where hand-crafted points fail. To index previous images, we adopt separate incremental binary Bag-of-Words (BoW) schemes for points and lines. Moreover, we adopt a binarization procedure for features’ descriptors to benefit from the advantages of learned features into a binary BoW model. Furthermore, image candidates from each BoW instance are merged using a novel query-adaptive late fusion approach. Finally, a spatial verification stage, which integrates appearance and geometry perspectives, allows us to enhance the global performance of the method. Our approach is validated using several public datasets, outperforming other state-of-the-art solutions in most cases, especially in low-textured scenarios.

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Section: Related Workmentioning

confidence: 99%

Appearance-based loop closure detection combining lines and learned points for low-textured environments

2022

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“…The presence of an GT ij = 1 element denotes the existence of a loop and GT ij = 0 otherwise. For the KITTI 00, 02, 05 and New College data sequences, the GT was manually generated in [79] through odometry information. In Lip6 Outdoor, this information is provided by the authors in [71].…”

Section: Ground Truthmentioning

confidence: 99%

Tracking‐DOSeqSLAM: A dynamic sequence‐based visual place recognition paradigm

Tsintotas

Bampis

Γαστεράτος

2021

IET Computer Vision

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Simultaneous localization and mapping (SLAM) refers to a process that permits a mobile robot to build up a map of the environment and, at the same time, to use it to compute its location. One of its most important components is its ability to associate the most recently perceived visual measurement to the one derived from previsited locations, a technique widely known as loop closure detection. In this article, we evolve our previous approach, dubbed as 'DOSeqSLAM' by presenting a low complexity loop closure detection pipeline wherein the traversed trajectory (map) is represented by sequencebased locations (submaps). Each of these groups of images, referred to as place, is generated online through a point tracking repeatability check employed on the perceived visual sensory information. When querying the database, the proper candidate place is selected and, through an image-to-image search, the appropriate location is chosen. The method is subjected to an extensive evaluation on seven publicly available datasets, revealing a substantial improvement in computational complexity and performance over its predecessors, while performing favourably against other state-of-the art solutions. The system's effectiveness is owed to the reduced number of places, which, compared to the original approach, is at least one order of magnitude less.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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“…This improvement allows the system to reduce the computational cost of operation and run on-line. Based on the visual features, Konstantinos et al [18] proposed the concept of Tracked Words, which are generated through feature tracking and matching in the sequence. The distance of Tracked Words is leveraged to set up a voting mechanism for loop closure detection.…”

Section: Introductionmentioning

confidence: 99%

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

Yuan

Zhou

et al. 2021

Remote Sensing

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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.

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Probabilistic Appearance-Based Place Recognition Through Bag of Tracked Words

Cited by 50 publications

References 31 publications

Appearance-based loop closure detection combining lines and learned points for low-textured environments

Appearance-based loop closure detection combining lines and learned points for low-textured environments

Tracking‐DOSeqSLAM: A dynamic sequence‐based visual place recognition paradigm

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

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