Visual Place Recognition in Long-term and Large-scale Environment based on CNN Feature

2018 ◽  
Author(s):  
Jianliang Zhu ◽  
Yunfeng Ai ◽  
Bin Tian ◽  
Dongpu Cao ◽  
Sebastian Scherer
Keyword(s):  
Large Scale ◽  
Place Recognition ◽  
Visual Place Recognition

scholarly journals Variational Bayesian Approach to Condition-Invariant Feature Extraction for Visual Place Recognition

2021 ◽  
Vol 11 (19) ◽  
pp. 8976
Author(s):  
Junghyun Oh ◽  
Gyuho Eoh
Keyword(s):  
Large Scale ◽  
Environmental Changes ◽  
Place Recognition ◽  
Stochastic Variational Inference ◽  
Invariant Features ◽  
Variational Autoencoder ◽  
Model Condition ◽  
Visual Place Recognition ◽  
Perceptual Changes

As mobile robots perform long-term operations in large-scale environments, coping with perceptual changes becomes an important issue recently. This paper introduces a stochastic variational inference and learning architecture that can extract condition-invariant features for visual place recognition in a changing environment. Under the assumption that a latent representation of the variational autoencoder can be divided into condition-invariant and condition-sensitive features, a new structure of the variation autoencoder is proposed and a variational lower bound is derived to train the model. After training the model, condition-invariant features are extracted from test images to calculate the similarity matrix, and the places can be recognized even in severe environmental changes. Experiments were conducted to verify the proposed method, and the experimental results showed that our assumption was reasonable and effective in recognizing places in changing environments.

scholarly journals Visual Place Recognition via Robust ℓ2-Norm Distance Based Holism and Landmark Integration

2019 ◽  
Vol 33 ◽  
pp. 8034-8041 ◽  
Author(s):  
Kai Liu ◽  
Hua Wang ◽  
Fei Han ◽  
Hao Zhang
Keyword(s):  
Large Scale ◽  
Optimization Problem ◽  
Data Sets ◽  
Place Recognition ◽  
Benchmark Data ◽  
Localization And Mapping ◽  
Novel Method ◽  
Visual Place Recognition ◽  
Holistic Representation

Visual place recognition is essential for large-scale simultaneous localization and mapping (SLAM). Long-term robot operations across different time of the days, months, and seasons introduce new challenges from significant environment appearance variations. In this paper, we propose a novel method to learn a location representation that can integrate the semantic landmarks of a place with its holistic representation. To promote the robustness of our new model against the drastic appearance variations due to long-term visual changes, we formulate our objective to use non-squared ℓ2-norm distances, which leads to a difficult optimization problem that minimizes the ratio of the ℓ2,1-norms of matrices. To solve our objective, we derive a new efficient iterative algorithm, whose convergence is rigorously guaranteed by theory. In addition, because our solution is strictly orthogonal, the learned location representations can have better place recognition capabilities. We evaluate the proposed method using two large-scale benchmark data sets, the CMU-VL and Nordland data sets. Experimental results have validated the effectiveness of our new method in long-term visual place recognition applications.

scholarly journals NYU-VPR: Long-Term Visual Place Recognition Benchmark with View Direction and Data Anonymization Influences

2021 ◽  
Author(s):  
Diwei Sheng ◽  
Yuxiang Chai ◽  
Xinru Li ◽  
Chen Feng ◽  
Jianzhe Lin ◽  
...  
Keyword(s):  
Place Recognition ◽  
Data Anonymization ◽  
Visual Place Recognition

Multiscale Convolutional Descriptor Aggregation for Visual Place Recognition

2020 ◽  
Vol 2020 (10) ◽  
pp. 313-1-313-7
Author(s):  
Raffaele Imbriaco ◽  
Egor Bondarev ◽  
Peter H.N. de With
Keyword(s):  
Image Matching ◽  
Large Scale ◽  
Place Recognition ◽  
Aggregation Method ◽  
Local Descriptors ◽  
Multi Scale ◽  
Vector Construction ◽  
Visual Place Recognition ◽  
Different Sources ◽  
Geometric Verification

Visual place recognition using query and database images from different sources remains a challenging task in computer vision. Our method exploits global descriptors for efficient image matching and local descriptors for geometric verification. We present a novel, multi-scale aggregation method for local convolutional descriptors, using memory vector construction for efficient aggregation. The method enables to find preliminary set of image candidate matches and remove visually similar but erroneous candidates. We deploy the multi-scale aggregation for visual place recognition on 3 large-scale datasets. We obtain a Recall@10 larger than 94% for the Pittsburgh dataset, outperforming other popular convolutional descriptors used in image retrieval and place recognition. Additionally, we provide a comparison for these descriptors on a more challenging dataset containing query and database images obtained from different sources, achieving over 77% Recall@10.

SRAL: Shared Representative Appearance Learning for Long-Term Visual Place Recognition

2017 ◽  
Vol 2 (2) ◽  
pp. 1172-1179 ◽  
Author(s):  
Fei Han ◽  
Xue Yang ◽  
Yiming Deng ◽  
Mark Rentschler ◽  
Dejun Yang ◽  
...  
Keyword(s):  
Place Recognition ◽  
Visual Place Recognition

scholarly journals Towards a Robust Visual Place Recognition in Large-Scale vSLAM Scenarios Based on a Deep Distance Learning

Sensors ◽  
2021 ◽  
Vol 21 (1) ◽  
pp. 310
Author(s):  
Liang Chen ◽  
Sheng Jin ◽  
Zhoujun Xia
Keyword(s):  
Distance Learning ◽  
Large Scale ◽  
Critical Role ◽  
Distance Constraint ◽  
Place Recognition ◽  
Learning Method ◽  
Localization And Mapping ◽  
Depth Analysis ◽  
Visual Place Recognition ◽  
Distance Relationship

The application of deep learning is blooming in the field of visual place recognition, which plays a critical role in visual Simultaneous Localization and Mapping (vSLAM) applications. The use of convolutional neural networks (CNNs) achieve better performance than handcrafted feature descriptors. However, visual place recognition is still a challenging task due to two major problems, i.e., perceptual aliasing and perceptual variability. Therefore, designing a customized distance learning method to express the intrinsic distance constraints in the large-scale vSLAM scenarios is of great importance. Traditional deep distance learning methods usually use the triplet loss which requires the mining of anchor images. This may, however, result in very tedious inefficient training and anomalous distance relationships. In this paper, a novel deep distance learning framework for visual place recognition is proposed. Through in-depth analysis of the multiple constraints of the distance relationship in the visual place recognition problem, the multi-constraint loss function is proposed to optimize the distance constraint relationships in the Euclidean space. The new framework can support any kind of CNN such as AlexNet, VGGNet and other user-defined networks to extract more distinguishing features. We have compared the results with the traditional deep distance learning method, and the results show that the proposed method can improve the performance by 19–28%. Additionally, compared to some contemporary visual place recognition techniques, the proposed method can improve the performance by 40%/36% and 27%/24% in average on VGGNet/AlexNet using the New College and the TUM datasets, respectively. It’s verified the method is capable to handle appearance changes in complex environments.

Long-Term Visual Robot Localization using Computational Models of the Neocortex

2020 ◽  
Author(s):  
Carlos Alexandre P. Pizzino ◽  
Patricia A. Vargas ◽  
Ramon R. Costa
Keyword(s):  
Computational Models ◽  
Internal Representation ◽  
Fixed Number ◽  
Place Recognition ◽  
Biologically Inspired ◽  
Hierarchical Temporal Memory ◽  
Representational Capacity ◽  
Visual Place Recognition ◽  
High Degree

Visual place recognition is an essential capability for autonomous mobile robots which use cameras as their primary sensors. Although there has been a considerable amount of research in the topic, the high degree of image variability poses extra research challenges. Following advances in neuroscience, new biologically inspired models have been developed. Inspired by the human neocortex, hierarchical temporal memory model has potential to identify temporal sequences of spatial patterns using sparse distributed representations, which are known to have high representational capacity and high tolerance to noise. These features are interesting for place recognition applications. Some authors have proposed simplifications from the original framework, such as starting from an empty set of minicolumns and increasing the number of minicolumns on demand instead of the usage of a fixed number of minicolumns whose connections adapt over time. In this paper, we investigate the usage of framework originally proposed with the aim of extending the run-time during long-term operations. Results show that the proposed architecture can encode an internal representation of the world using a fixed number of cells in order to improve system scalability.

scholarly journals Learning Context Flexible Attention Model for Long-Term Visual Place Recognition

2018 ◽  
Vol 3 (4) ◽  
pp. 4015-4022 ◽  
Author(s):  
Zetao Chen ◽  
Lingqiao Liu ◽  
Inkyu Sa ◽  
Zongyuan Ge ◽  
Margarita Chli
Keyword(s):  
Place Recognition ◽  
Learning Context ◽  
Attention Model ◽  
Visual Place Recognition

scholarly journals Place Recognition: An Overview of Vision Perspective

2018 ◽  
Vol 8 (11) ◽  
pp. 2257 ◽  
Author(s):  
Zhiqiang Zeng ◽  
Jian Zhang ◽  
Xiaodong Wang ◽  
Yuming Chen ◽  
Chaoyang Zhu
Keyword(s):  
Place Recognition ◽  
Image Description ◽  
Query Image ◽  
Recognition Systems ◽  
Image Representations ◽  
A New Technique ◽  
Visual Place Recognition ◽  
Future Work ◽  
Traditional Image

Place recognition is one of the most fundamental topics in the computer-vision and robotics communities, where the task is to accurately and efficiently recognize the location of a given query image. Despite years of knowledge accumulated in this field, place recognition still remains an open problem due to the various ways in which the appearance of real-world places may differ. This paper presents an overview of the place-recognition literature. Since condition-invariant and viewpoint-invariant features are essential factors to long-term robust visual place-recognition systems, we start with traditional image-description methodology developed in the past, which exploits techniques from the image-retrieval field. Recently, the rapid advances of related fields, such as object detection and image classification, have inspired a new technique to improve visual place-recognition systems, that is, convolutional neural networks (CNNs). Thus, we then introduce the recent progress of visual place-recognition systems based on CNNs to automatically learn better image representations for places. Finally, we close with discussions and mention of future work on place recognition.

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