Visual Localizer: Outdoor Localization Based on ConvNet Descriptor and Global Optimization for Visually Impaired Pedestrians

Lin, Shufei; Cheng, Ruiqi; Wang, Kaiwei; Yang, Kailun

doi:10.3390/s18082476

Cited by 39 publications

(33 citation statements)

References 47 publications

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“…where R = T P/(T P + F N ) and P = T P/(T P + F P ). According to our previous research [5], the deep descriptors derived from GoogLeNet [35] pretrained on Im-ageNet [32] is the optimal choice on the task of visual localization among the prevailing networks. Therefore, the five optimal feature maps of convolutional or pooling layers in GoogLeNet (listed in Table I) are selected as one of the baseline of passive descriptors.…”

Section: A Comparison Between Passive and Active Deep Descriptorsmentioning

confidence: 99%

Panoramic Annular Localizer: Tackling the Variation Challenges of Outdoor Localization Using Panoramic Annular Images and Active Deep Descriptors

Cheng

Wang

Lin

et al. 2019

2019 IEEE Intelligent Transportation Systems Conference (ITSC)

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Visual localization is an attractive problem that estimates the camera localization from database images based on the query image. It is a crucial task for various applications, such as autonomous vehicles, assistive navigation and augmented reality. The challenging issues of the task lie in various appearance variations between query and database images, including illumination variations, dynamic object variations and viewpoint variations. In order to tackle those challenges, Panoramic Annular Localizer into which panoramic annular lens and robust deep image descriptors are incorporated is proposed in this paper. The panoramic annular images captured by the single camera are processed and fed into the NetVLAD network to form the active deep descriptor, and sequential matching is utilized to generate the localization result. The experiments carried on the public datasets and in the field illustrate the validation of the proposed system.

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Section: A Comparison Between Passive and Active Deep Descriptorsmentioning

confidence: 99%

Panoramic Annular Localizer: Tackling the Variation Challenges of Outdoor Localization Using Panoramic Annular Images and Active Deep Descriptors

Cheng

Wang

Lin

et al. 2019

2019 IEEE Intelligent Transportation Systems Conference (ITSC)

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“…The appearance variations impede the performance of visual place recognition, and many researchers are dedicated to mitigating the impact of appearance variations towards place recognition by different methods [8,9,12,18]. The illumination change is one of vital appearance variations, and quite a few place recognition algorithms [19,20] addressed the issue.…”

Section: State Of the Artmentioning

confidence: 99%

“…The concrete extraction configurations of those descriptors have been illustrated in our previous work [9,8].…”

Section: Multiple Descriptors Extraction From Multimodal Imagesmentioning

confidence: 99%

OpenMPR: Recognize places using multimodal data for people with visual impairments

Cheng

Wang

Bai

et al. 2019

Meas. Sci. Technol.

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Place recognition plays a crucial role in navigational assistance, and is also a challenging issue of assistive technology. The place recognition is prone to erroneous localization owing to various changes between database and query images. Aiming at the wearable assistive device for visually impaired people, we propose an open-sourced place recognition algorithm OpenMPR, which utilizes the multimodal data to address the challenging issues of place recognition.Compared with conventional place recognition, the proposed OpenMPR not only leverages multiple effective descriptors, but also assigns different weights to those descriptors in image matching. Incorporating GNSS data into the algorithm, the cone-based sequence searching is used for robust place recognition. The experiments illustrate that the proposed algorithm manages to solve the place recognition issue in the real-world scenarios and surpass the state-of-the-art algorithms in terms of assistive navigation performance. On the real-world testing dataset, the online OpenMPR achieves 88.7% precision at 100% recall without illumination changes, and achieves 57.8% precision at 99.3% recall with illumination changes. The OpenMPR is available at https://github.com/chengricky/OpenMultiPR.

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“…These reasons have promoted this new localization concept based on LPS especially for high accuracy automated navigation [1,2]. LPS require the deployment of architecture sensors in a defined and known space where the capabilities of the system are maximized.…”

Section: Introductionmentioning

confidence: 99%

Local Wireless Sensor Networks Positioning Reliability Under Sensor Failure

Díez-González

Álvarez

Prieto-Fernández

et al. 2020

Sensors

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Local Positioning Systems are collecting high research interest over the last few years. Its accurate application in high-demanded difficult scenarios has revealed its stability and robustness for autonomous navigation. In this paper, we develop a new sensor deployment methodology to guarantee the system availability in case of a sensor failure of a five-node Time Difference of Arrival (TDOA) localization method. We solve the ambiguity of two possible solutions in the four-sensor TDOA problem in each combination of four nodes of the system by maximizing the distance between the two possible solutions in every target possible location. In addition, we perform a Genetic Algorithm Optimization in order to find an optimized node location with a trade-off between the system behavior under failure and its normal operating condition by means of the Cramer Rao Lower Bound derivation in each possible target location. Results show that the optimization considering sensor failure enhances the average values of the convergence region size and the location accuracy by 31% and 22%, respectively, in case of some malfunction sensors regarding to the non-failure optimization, only suffering a reduction in accuracy of less than 5% under normal operating conditions.

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Visual Localizer: Outdoor Localization Based on ConvNet Descriptor and Global Optimization for Visually Impaired Pedestrians

Cited by 39 publications

References 47 publications

Panoramic Annular Localizer: Tackling the Variation Challenges of Outdoor Localization Using Panoramic Annular Images and Active Deep Descriptors

Panoramic Annular Localizer: Tackling the Variation Challenges of Outdoor Localization Using Panoramic Annular Images and Active Deep Descriptors

OpenMPR: Recognize places using multimodal data for people with visual impairments

Local Wireless Sensor Networks Positioning Reliability Under Sensor Failure

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