Semantics-aware visual localization under challenging perceptual conditions

Naseer, Tayyab; Oliveira, Gabriel L.; Brox, Thomas; Burgard, Wolfram

doi:10.1109/icra.2017.7989305

Cited by 133 publications

(136 citation statements)

References 19 publications

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“…Generally speaking, image representation learning can fall into two main categories in visual place recognition. The first category describes the whole image using a holistic feature [3], [8], [11], [15], [16]. The second one describes the image with a set of local features.…”

Section: Introductionmentioning

confidence: 99%

“…Kim et al [9] learned to generate image representations incorporating context-aware feature preponderance. Naseer et al [11] employed semantic segmentation to extract meaningful features from buildings. However, the method in [11] relies on supervised priors, which is of limited use if the scenes do not contain categories in semantic segmentation.…”

Section: Introductionmentioning

confidence: 99%

“…Naseer et al [11] employed semantic segmentation to extract meaningful features from buildings. However, the method in [11] relies on supervised priors, which is of limited use if the scenes do not contain categories in semantic segmentation. All the above approaches [9], [11] generate holistic features to describe the entire image, which lack of the ability for dealing with partial occlusion and background clutters.…”

Section: Introductionmentioning

confidence: 99%

“…However, the method in [11] relies on supervised priors, which is of limited use if the scenes do not contain categories in semantic segmentation. All the above approaches [9], [11] generate holistic features to describe the entire image, which lack of the ability for dealing with partial occlusion and background clutters. Recently, Noh et al [6] proposed an attention based network named DELF for the task of image retrieval.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Localizing Discriminative Visual Landmarks for Place Recognition

Xin

Cai

Lü

et al. 2019

2019 International Conference on Robotics and Automation (ICRA)

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We address the problem of visual place recognition with perceptual changes. The fundamental problem of visual place recognition is generating robust image representations which are not only insensitive to environmental changes but also distinguishable to different places. Taking advantage of the feature extraction ability of Convolutional Neural Networks (CNNs), we further investigate how to localize discriminative visual landmarks that positively contribute to the similarity measurement, such as buildings and vegetations. In particular, a Landmark Localization Network (LLN) is designed to indicate which regions of an image are used for discrimination. Detailed experiments are conducted on open source datasets with varied appearance and viewpoint changes. The proposed approach achieves superior performance against state-of-the-art methods.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Localizing Discriminative Visual Landmarks for Place Recognition

Xin

Cai

Lü

et al. 2019

2019 International Conference on Robotics and Automation (ICRA)

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show abstract

“…al. [24] exploited geometrically robust regions to determine the position of the input image, which is a different task from localization.…”

Section: Introductionmentioning

confidence: 99%

Learning Local Feature Descriptor with Motion Attribute For Vision-based Localization

Song

Zhu

et al. 2019

2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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In recent years, camera-based localization has been widely used for robotic applications, and most proposed algorithms rely on local features extracted from recorded images. For better performance, the features used for openloop localization are required to be short-term globally static, and the ones used for re-localization or loop closure detection need to be long-term static. Therefore, the motion attribute of a local feature point could be exploited to improve localization performance, e.g., the feature points extracted from moving persons or vehicles can be excluded from these systems due to their unsteadiness. In this paper, we design a fully convolutional network (FCN), named MD-Net, to perform motion attribute estimation and feature description simultaneously. MD-Net has a shared backbone network to extract features from the input image and two network branches to complete each sub-task. With MD-Net, we can obtain the motion attribute while avoiding increasing much more computation. Experimental results demonstrate that the proposed method can learn distinct local feature descriptor along with motion attribute only using an FCN, by outperforming competing methods by a wide margin. We also show that the proposed algorithm can be integrated into a vision-based localization algorithm to improve estimation accuracy significantly.

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Set‐theoretic localization for mobile robots with infrastructure‐based sensing

et al. 2022

Adv Control Appl

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In this article, we propose a set-membership based localization approach for mobile robots using infrastructure-based sensing. Under an assumption of known uncertainties bounds of the noise in the sensor measurement and robot motion models, the proposed method computes uncertainty sets that over-bound the robot 2D body and orientation via set-valued motion propagation and subsequent measurement update from infrastructure-based sensing. We establish theoretical properties and computational approaches for this set-theoretic localization method and illustrate its application to an automated valet parking example in simulations, and to omnidirectional robot localization problems in real-world experiments. With deteriorating uncertainties in system parameters and initialization parameters, we conduct sensitivity analysis and demonstrate that the proposed method, in comparison to the FastSLAM, has a milder performance degradation, thus is more robust against the changes in the parameters. Meanwhile, the proposed method can provide estimates with smaller standard deviation values.

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Semantics-aware visual localization under challenging perceptual conditions

Cited by 133 publications

References 19 publications

Localizing Discriminative Visual Landmarks for Place Recognition

Localizing Discriminative Visual Landmarks for Place Recognition

Learning Local Feature Descriptor with Motion Attribute For Vision-based Localization

Set‐theoretic localization for mobile robots with infrastructure‐based sensing

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