DOSeqSLAM: Dynamic On-line Sequence Based Loop Closure Detection Algorithm for SLAM

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

doi:10.1109/ist.2018.8577113

Cited by 21 publications

(20 citation statements)

References 22 publications

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“…The article in hand extends our previous work [34], presenting an appearance-and sequence-based loop closure detection method, which makes use of KLT tracking in order to efficiently fragment the robot's map into submaps defining dynamic places, dubbed as Tracking-DOSeqSLAM3. Following its ancestor's image representation and similarity comparison processes, the proposed pipeline highlights the system's ability to recognise previsited places using almost two orders of magnitude less operations.…”

Section: Discussionmentioning

confidence: 68%

“…Recall ¼ True positive True positive þ False negative : ð9Þ TA B L E 3 Recall rates at 100% precision: a comparison of the proposed method against our previous work [34], as well as the baseline approach of SeqSLAM [21].Bold values indicate the maximum performance per evaluated image sequence. As shown from the obtained results, the proposed pipeline outperforms the previous versions, while performance improvement is observed as the extracted set of points increases until a certain point.…”

Section: Precision-recall Metricmentioning

confidence: 99%

“…The minimum score s do is selected for each instance in the navigated path, yielding an S do vector, wherein the lowest value is selected for the particular location I j . Subsequently, this score is normalised over the second lowest value outside of a window W DOSeq [34] resulting to γ. Lastly, an average weighted filter is applied for the final decision. A candidate loop closure sequence is determined when factor γ is satisfied and the system performs an additional greedy image-to-image search into the SAD submatrix for singleimage associations.…”

Section: Sequence-based Searchmentioning

confidence: 99%

Section: Precision-recall Metricmentioning

confidence: 99%

“…The query frame is the image recorded by the vehicle at time t, whereas the matched frame is the corresponding one identified among the members of the chosen place. From left to right: Lip6 Outdoor [71], New College [93], City Centre [19] and KITTI 02 [92] TA B L E 4 Processing time per image (ms/query) of Tracking-DOSeqSLAM, as well as of its previous version [34] and the baseline approach [21], for the KITTI 00 data sequence.It is notable that the proposed pipeline requires less time due to its efficient matching process which is based on the image aggregation from the generated places.…”

Section: Performance Evaluationmentioning

confidence: 99%

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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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Section: Discussionmentioning

confidence: 68%

Section: Precision-recall Metricmentioning

confidence: 99%

Section: Sequence-based Searchmentioning

confidence: 99%

Section: Precision-recall Metricmentioning

confidence: 99%

Section: Performance Evaluationmentioning

confidence: 99%

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

Tsintotas

Bampis

Γαστεράτος

2021

IET Computer Vision

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Visual Place Recognition for Simultaneous Localization and Mapping

Tsintotas¹,

Bampis²,

Γαστεράτος³

2022

Autonomous Vehicles Volume 2

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Fast and incremental loop closure detection with deep features and proximity graphs

Zhu

Wei

et al. 2022

Journal of Field Robotics

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In recent years, the robotics community has extensively examined methods concerning the place recognition task within the scope of simultaneous localization and mapping applications. This article proposes an appearance‐based loop closure detection pipeline named “Fast and Incremental Loop closure Detection (FILD++). First, the system is fed by consecutive images and, via passing them twice through a single convolutional neural network, global and local deep features are extracted. Subsequently, a hierarchical navigable small‐world graph incrementally constructs a visual database representing the robot's traversed path based on the computed global features. Finally, a query image, grabbed each time step, is set to retrieve similar locations on the traversed route. An image‐to‐image pairing follows, which exploits local features to evaluate the spatial information. Thus, in the proposed article, we propose a single network for global and local feature extraction in contrast to our previous work (FILD), while an exhaustive search for the verification process is adopted over the generated deep local features avoiding the utilization of hash codes. Exhaustive experiments on eleven publicly available data sets exhibit the system's high performance (achieving the highest recall score on eight of them) and low execution times (22.05 ms on average in New College, which is the largest one containing 52,480 images) compared to other state‐of‐the‐art approaches.

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DOSeqSLAM: Dynamic On-line Sequence Based Loop Closure Detection Algorithm for SLAM

Cited by 21 publications

References 22 publications

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

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

Visual Place Recognition for Simultaneous Localization and Mapping

Fast and incremental loop closure detection with deep features and proximity graphs

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