High performance loop closure detection using bag of word pairs

Kejriwal, Nishant; Kumar, Swagat; Shibata, Tomohiro

doi:10.1016/j.robot.2015.12.003

Cited by 57 publications

(46 citation statements)

References 14 publications

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“…The appearance-only based methods like FAB-MAP [9] use visual Bag of Words (BoW) approach to construct a visual vocabulary using robust features such as SURF [10]. The appearance-based methods are often supplemented with geometric information [12] to further improve the robustness of the system. However, recognizing places in challenging environmental conditions such as varying season, time of day and weather conditions, is a challenging task.…”

Section: A Visual Place Recognitionmentioning

confidence: 99%

“…The appearance-robust methods like SeqSLAM [3] are invariant to challenging environmental conditions, but at the cost of viewpoint-dependence and velocity-sensitivity. The use of hand-crafted local features like SURF [10] or global image representations like HoG [11] for visual place recognition [12], [13] respectively, is continually being replaced by deep-learned feature representations [14], [15].…”

Section: Introductionmentioning

confidence: 99%

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Don't Look Back: Robustifying Place Categorization for Viewpoint- and Condition-Invariant Place Recognition

Garg

Suenderhauf

Milford

2018

2018 IEEE International Conference on Robotics and Automation (ICRA)

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When a human drives a car along a road for the first time, they later recognize where they are on the return journey typically without needing to look in their rear view mirror or turn around to look back, despite significant viewpoint and appearance change. Such navigation capabilities are typically attributed to our semantic visual understanding of the environment [1] beyond geometry to recognizing the types of places we are passing through such as "passing a shop on the left" or "moving through a forested area". Humans are in effect using place categorization [2] to perform specific place recognition even when the viewpoint is 180 degrees reversed. Recent advances in deep neural networks have enabled high performance semantic understanding of visual places and scenes, opening up the possibility of emulating what humans do. In this work, we develop a novel methodology for using the semantics-aware higher-order layers of deep neural networks for recognizing specific places from within a reference database. To further improve the robustness to appearance change, we develop a descriptor normalization scheme that builds on the success of normalization schemes for pure appearance-based techniques such as SeqSLAM [3]. Using two different datasets -one road-based, one pedestrian-based, we evaluate the performance of the system in performing place recognition on reverse traversals of a route with a limited field of view camera and no turn-back-and-look behaviours, and compare to existing stateof-the-art techniques and vanilla off-the-shelf features. The results demonstrate significant improvements over the existing state of the art, especially for extreme perceptual challenges that involve both great viewpoint change and environmental appearance change. We also provide experimental analyses of the contributions of the various system components: the use of spatio-temporal sequences, place categorization and placecentric characteristics as opposed to object-centric semantics.

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Section: A Visual Place Recognitionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Don't Look Back: Robustifying Place Categorization for Viewpoint- and Condition-Invariant Place Recognition

Garg

Suenderhauf

Milford

2018

2018 IEEE International Conference on Robotics and Automation (ICRA)

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“…Besides its efficiency in recognizing the object, this method is fast and easier to implement. It can be improve so that it can be robust to the occlusion object, clutter, non-rigid deformation and viewpoint change [19,20].…”

Section: Bag Of Word T Echniquesmentioning

confidence: 99%

A Performance Evaluation of Content-Based Image Retrieval for Scene Categorization

2017

Aug. 8-9, 2017 Singapore Back RTET-17, CFBHS-17, IECMAE-17, AESTWM-17, LHHSS-17 &Amp; LEBCSR-17

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Abstract-Content-based image retrieval remains a critical problem in computer vision. In this paper, we study the performance of various content-based image retrieval technique for recognizing the object and scene. We conduct the comparative survey to compare the state of the art bag of words (BOW) framework with other method to help the researcher to understand more and enable researcher selecting the most suitable technique. We carried out the experiment and tested with 3 publicly dataset that are Caltech 101, Caltech 256 and 15-Scene Category dataset with BOW method. We also compare and evaluate the effect on the number of cluster toward the computational time and the accuracy. In addition, the significant of different feature extraction method ap plied in BOW performance is analyzed. In conclusion, we discuss on several key potential research topics towards the content-based image retrieval.

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“…Nonetheless, these models are strictly content-based and do not take into account spatial 978-1-7281-3605-9/19/$31.00 c 2019 IEEE relationships between image features. It is for this reason that they are usually fairly robust to viewpoint changes but suffer from problems such as perceptual aliasing [17].…”

Section: Introductionmentioning

confidence: 99%

Spatio-Semantic ConvNet-Based Visual Place Recognition

Camara

Přeučil

2019

2019 European Conference on Mobile Robots (ECMR)

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We present a Visual Place Recognition system that follows the two-stage format common to image retrieval pipelines. The system encodes images of places by employing the activations of different layers of a pre-trained, off-the-shelf, VGG16 Convolutional Neural Network (CNN) architecture. In the first stage of our method and given a query image of a place, a number of top candidate images is retrieved from a previously stored database of places. In the second stage, we propose an exhaustive comparison of the query image against these candidates by encoding semantic and spatial information in the form of CNN features. Results from our approach outperform by a large margin state-of-the-art visual place recognition methods on five of the most commonly used benchmark datasets. The performance gain is especially remarkable on the most challenging datasets, with more than a twofold recognition improvement with respect to the latest published work.

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High performance loop closure detection using bag of word pairs

Cited by 57 publications

References 14 publications

Don't Look Back: Robustifying Place Categorization for Viewpoint- and Condition-Invariant Place Recognition

Don't Look Back: Robustifying Place Categorization for Viewpoint- and Condition-Invariant Place Recognition

A Performance Evaluation of Content-Based Image Retrieval for Scene Categorization

Spatio-Semantic ConvNet-Based Visual Place Recognition

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