scholarly journals Learning of Holism-Landmark Graph Embedding for Place Recognition in Long-Term Autonomy

2018 ◽  
Vol 3 (4) ◽  
pp. 3669-3676 ◽  
Author(s):  
Fei Han ◽  
Saad El Beleidy ◽  
Hua Wang ◽  
Cang Ye ◽  
Hao Zhang
Keyword(s):  
Graph Embedding ◽  
Place Recognition

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

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.

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 Radar-to-Lidar: Heterogeneous Place Recognition via Joint Learning

2021 ◽  
Vol 8 ◽  
Author(s):  
Huan Yin ◽  
Xuecheng Xu ◽  
Yue Wang ◽  
Rong Xiong
Keyword(s):  
Source Code ◽  
Light Detection And Ranging ◽  
Place Recognition ◽  
Learning Stage ◽  
Joint Learning ◽  
Adverse Conditions ◽  
Testing Stage ◽  
Single Sensor ◽  
Public Datasets

Place recognition is critical for both offline mapping and online localization. However, current single-sensor based place recognition still remains challenging in adverse conditions. In this paper, a heterogeneous measurement based framework is proposed for long-term place recognition, which retrieves the query radar scans from the existing lidar (Light Detection and Ranging) maps. To achieve this, a deep neural network is built with joint training in the learning stage, and then in the testing stage, shared embeddings of radar and lidar are extracted for heterogeneous place recognition. To validate the effectiveness of the proposed method, we conducted tests and generalization experiments on the multi-session public datasets and compared them to other competitive methods. The experimental results indicate that our model is able to perform multiple place recognitions: lidar-to-lidar (L2L), radar-to-radar (R2R), and radar-to-lidar (R2L), while the learned model is trained only once. We also release the source code publicly: https://github.com/ZJUYH/radar-to-lidar-place-recognition.

scholarly journals Role of type I interferon signaling and microglia in the abnormal long term potentiation and object place recognition deficits of male mice with a mutation of the Tuberous Sclerosis 2 gene

2021 ◽  
Author(s):  
Manuel F López-Aranda ◽  
Gayle M Boxx ◽  
Miranda Phan ◽  
Karen Bach ◽  
Rochelle Mandanas ◽  
...  
Keyword(s):  
Type I Interferon ◽  
Long Term Potentiation ◽  
Null Mutation ◽  
Type I ◽  
Place Recognition ◽  
Interferon Signaling ◽  
Male Mice ◽  
Term Potentiation

Tuberous Sclerosis Complex (TSC) is a genetic disorder associated with high rates of intellectual disability and autism. Although previous studies focused on the role of neuronal deficits in the memory phenotypes of rodent models of TSC, the results presented here demonstrate a role for microglia in these deficits. Mice with a heterozygous null mutation of the Tsc2 gene (Tsc2+/-), show deficits in hippocampal dependent tasks, as well as abnormal long-term potentiation (LTP) in the hippocampal CA1 region. Here, we show that microglia and type I interferon signaling (IFN1) have a key role in the object place recognition (OPR; a hippocampal dependent task) deficits and abnormal LTP of Tsc2+/- male mice. Unexpectedly, we demonstrate that male, but not female, Tsc2+/- mice showed OPR deficits. Importantly, these deficits can be rescued by depletion of microglia, as well as by a genetic manipulation of a signaling pathway known to modulate microglia function (interferon-alpha/beta receptor alpha chain null mutation). In addition to rescuing the OPR deficits, depletion of microglia also reversed the abnormal LTP of the Tsc2+/- mice. Altogether, our results suggest that altered IFN1 signaling in microglia cause the abnormal LTP and OPR deficits of male Tsc2+/- mice.

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