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Evaluating and Optimizing Feature Combinations for visual loop closure detection

JOURNAL OF INTELLIGENT & ROBOTIC SYSTEMS 2022年第2期104卷 1-16页

作者： Wang, Xiaolong Zhang, Hong Peng, Guohua Shaanxi Normal Univ Sch Math & Stat Xian 710119 Peoples R China Univ Alberta Dept Comp Sci Edmonton AB Canada Northwestern Polytech Univ Sch Math & Stat Xian 710072 Peoples R China

loop closure detection (LCD) is a key step in visual simultaneous localization and mapping systems to correct the map and relocalize the vehicle. However, it may fail when illumination variations and shifting dynamics present in the scenes. One way to improve the precision is to effectively combine multiple image features that supply complementary information. In this paper, a general method to quantitatively measure the efficacy of individual image features as well as feature combinations for LCD is proposed by calculating the statistical distance considering the distributions of feature vectors. Based on different statistical distances including Kullback-Leibler divergence, Bhattacharyya divergence and Wasserstein metric, various numerical indices capable of evaluating feature combinations are obtained and compared. An unsupervised algorithm is further proposed to optimize feature combinations by maximizing any of the indices. Experiments show that the proposed indices can measure the efficacies of image features and the resulting feature combinations maximizing the indices can improve the precision of LCD.

关键词： visual loop closure detection Feature combination Feature evaluation Kullback-Leibler divergence Bhattacharyya divergence Wasserstein metric

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PlaceNet: A multi-scale semantic-aware model for visual loop closure detection

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2023年第1期119卷

作者： Osman, Hussein Darwish, Nevin Bayoumi, AbdElMoniem Cairo Univ Fac Engn Dept Comp Engn Gamaa St Giza 12613 Egypt Ecole Polytech Fed Lausanne EPFL INN 134 Batiment INNStn 14 CH-1015 Lausanne Switzerland

loop closure detection helps simultaneous localization and mapping systems reduce map and state uncertainty via recognizing previously visited places along the path of a mobile robot. However, visual loop closure detection is susceptible to scenes with dynamic objects and changes in illumination, background, and weather conditions. This paper introduces PlaceNet, a novel plug-and-play model for visual loop closure detection. PlaceNet is a multi-scale deep autoencoder network augmented with a semantic fusion layer for scene understanding. The main idea of PlaceNet is to learn where not to look in a dynamic scene full of moving objects, i.e., avoid being distracted by dynamic objects to focus on the scene landmarks instead. We train PlaceNet to identify dynamic objects in scenes via learning a grayscale semantic map indicating the position of static and moving objects in the image. PlaceNet generates semantic-aware deep features that are robust to dynamic environments and scale invariant. We evaluated our method on different challenging indoor and outdoor benchmarks. To conclude, PlaceNet demonstrated competitive results compared to the state-of-the-art methods over various datasets used in our experiments.

关键词： visual loop closure detection Deep learning for visual perception visual SLAM Localization

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Improved Deep Distance Learning for visual loop closure detection in Smart City

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PEER-TO-PEER NETWORKING AND APPLICATIONS 2020年第4期13卷 1260-1271页

作者： Jin, Sheng Gao, Yu Chen, Liang Soochow Univ Sch Mech & Elect Engn Suzhou 215021 Peoples R China

visual Simultaneous Localization and Mapping (vSLAM) are expected to promote the initiatives in Smart City including driverless cars and intelligent robots. loop closure detection (LCD) is an important module in a vSLAM system. Existing works with convolutional neural networks exhibit better performance on feature extraction, but this is far from enough. Concerning the characteristics of LCD, it is of great significance to have a customized loss function and a method to construct suitable training image sets. Based on this motivation, we propose a novel framework for LCD. Through a deep analysis of the distance relationships in the LCD problem, we propose the multi-tuplet clusters loss function together with mini-batch construction scheme. The proposed framework can map images to a low dimensional space and extract more discriminative image features, which help learn a more essential distance relationship of the LCD problem. Extensive evaluations demonstrate that our method outperforms many state-of-art approaches even in complex environments with strong appearance changes. Importantly, though the training process is computationally demanding, its online application is very efficient.

关键词： visual loop closure detection Simultaneous localization and mapping Deep distance learning

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visual loop closure detection over Illumination Change 16

Visual Loop Closure Detection over Illumination Change

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16th International Conference on Ubiquitous Robots (UR)

作者： Lee, Seongwon Jo, HyungGi Cho, Hae Min Kim, Euntai Yonsei Univ Dept Elect & Elect Engn Seoul 03722 South Korea

ISBN: (纸本)9781728132327

In the Simultaneous Localization and Mapping (SLAM) problem, loop closure detection is a task of whether the robot has visited the area or not, when robot has traveled a long distance, and then revisits the previous travel route. Bag-of-visual-words method, one of the popular and fast visual loop closure detection method, converts a query image into a descriptor and compares it with the descriptors of the whole database images to determine loop closure. However, bag-of-visual words method has lower performance when the illumination is changed while a long driving time because the characteristic of the image is changed due to the illumination difference. In this paper, we propose a novel loop closure detection method robust to illumination change through fusing the results of parallel loop closing detection in original color space and illumination invariant space both by generating illumination invariant space image based codebook. Experimental results show that the proposed algorithm robustly performs loop closure detection over illumination change than conventional method.

关键词： visual loop closure detection illumination change Bag of visual Words (BoVW) illumination invariant space transform

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Patterns of approximated localised moments for visual loop closure detection

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IET COMPUTER VISION 2017年第3期11卷 237-245页

作者： Erhan, Can Sariyanidi, Evangelos Sencan, Onur Temeltas, Hakan Istanbul Tech Univ Dept Control & Automat Engn TR-34469 Istanbul Turkey Queen Mary Univ London Sch Elect Engn & Comp Sci Mile End Rd London E1 4NS England

In the context of autonomous mobile robot navigation, loop closing is defined as the correct identification of a previously visited location. loop closing is essential for the accurate self-localisation of a robot;however, it is also challenging due to perceptual aliasing, which occurs when the robot traverses in environments with visually similar places (e.g. forests, parks, office corridors). In this study, the authors apply the local Zernike moments (ZMs) for loop closure detection. When computed locally, ZMs provide a high discrimination ability, which enables the distinguishing of similar-looking places. Particularly, they show that increasing the density over which the local ZMs are computed improves loop closing accuracy significantly. Furthermore, they present an approximation of ZMs that allows the usage of integral images, which enable real-time operation. Experiments on real datasets with strong perceptual aliasing show that the proposed ZM-based descriptor outperforms state-of-the-art methods in terms of loop closure accuracy. They also release the source-code of the implementation for research purposes.

关键词： object detection path planning mobile robots SLAM (robots) visual loop closure detection autonomous mobile robot navigation loop closing perceptual aliasing Zernike moments integral images ZM-based descriptor

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Leveraging Semantics in Appearance based loop closure detection for Long- Term visual SLAM

Leveraging Semantics in Appearance based Loop Closure Detect...

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IEEE International Conference on Big Data and Smart Computing (BigComp)

作者： Arshad, Saba Kim, Gon-Woo Chungbuk Natl Univ Dept Control & Robot Engn Cheongju South Korea

ISBN: (纸本)9781665475785

In simultaneous localization and mapping, the detection of true loop closure benefits in relocalization and increased map accuracy. However, its performance is largely affected by variation in light conditions, viewpoints, seasons, and presence of dynamic objects. Focusing the advantages of visual semantics, this research proposes a semantics aided bag-of-wordsmodel overcoming the limitations of conventional BoW model which performs appearance-based loop detection and has been gold standard for many years. The proposed method reduces the computation time while matching current frame with the database image by segmenting the search space. Further it enhances the true loop detection rate.

关键词： visual loop closure detection semantic labels long term autonomy visual navigation

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Combining Multiple Image Descriptions for loop closure detection

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JOURNAL OF INTELLIGENT & ROBOTIC SYSTEMS 2018年第3-4期92卷 565-585页

作者： Wang, Xiaolong Peng, Guohua Zhang, Hong Northwestern Polytech Univ Sch Nat & Appl Sci Dept Appl Math Xian 710072 Shaanxi Peoples R China Univ Alberta Dept Comp Sci Edmonton AB Canada

The success of visual loop closure detection depends on the discrimination ability of the image descriptions. Different sources of image descriptions may carry complementary information as well as redundant information. Though integrating them properly can be beneficial, a main obstacle is the lack of analytical quality indicators to weigh different descriptions jointly. Inspired by the linear discriminant analysis, we propose an efficacy index to evaluate the weighted linear combinations of multiple image descriptions for loop closure detection. When a collection of image descriptions is given, the optimal weights maximizing the efficacy index are deduced analytically. As negative weights may negatively affect the performance of detection, a gradient descent algorithm is further proposed to jointly optimize the nonnegative weights. We use the proposed weighting strategies to combine the image descriptions extracted from multiple local image patches by multiple descriptor extractors. It is experimentally demonstrated that our weighted combinations of image descriptions can greatly improve the performance of loop closure detection by emphasizing informative components and de-emphasizing redundant components.

关键词： visual loop closure detection Feature selection Weighted linear combination Linear discriminant analysis Nonnegative optimization

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On the Use of Binary Feature Descriptors for loop closure detection 19

On the Use of Binary Feature Descriptors for Loop Closure De...

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19th IEEE International Conference on Emerging Technology and Factory Automation (ETFA)

作者： Garcia-Fidalgo, Emilio Ortiz, Alberto Univ Balearic Isl Dept Math & Comp Sci Palma de Mallorca 07122 Spain

ISBN: (纸本)9781479948451

We propose an appearance-based loop closure detection algorithm based on binary features and a Bag-of-Words scheme. Unlike other approaches that build the visual dictionary offline, we introduce an indexing method for binary features, which, in combination with an inverted index, enable us to obtain loop closure candidates in an online manner. These structures are used in a discrete Bayes filter to select final loop candidates and to ensure temporal coherency between predictions. Our approach is validated using two publicly available datasets of outdoor environments and compared with the state-of-the-art FAB-MAP algorithm, showing very promising results and demonstrating that binary features can be used for visual loop closure detection.

关键词： filtering theory image representation object detection BoW FAB-MAP algorithm appearance-based loop closure detection algorithm bag-of-words scheme binary feature descriptors discrete Bayes filter fast appearance-based mapping image sparse vector representation indexing method inverted index temporal coherency visual loop closure detection Cameras Dictionaries Feature extraction Indexes Robots visualization Vocabulary

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