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

Cheng, Ruiqi; Wang, Kaiwei; Lin, Shufei; Hu, Weijian; Yang, Kailun; Huang, Xiao; Li, Huabing; Sun, Dongming; Bai, Jian

doi:10.1109/itsc.2019.8917508

Cited by 25 publications

(21 citation statements)

References 37 publications

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“…The proposed multimodal sensor has already been used in diverse intelligent vehicles systems for panoramic scene parsing, 3 visual topological localization 22 and nighttime semantic understanding. 23,24 In the future, we aim to optimize the speed and deploy our sensor in more transportation applications.…”

Section: Discussionmentioning

confidence: 99%

A multimodal vision sensor for autonomous driving

Sun

Huang

Yang

2019

Counterterrorism, Crime Fighting, Forensics, and Surveillance Technologies III

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This paper describes a multimodal vision sensor that integrates three types of cameras, including a stereo camera, a polarization camera and a panoramic camera. Each sensor provides a specific dimension of information: the stereo camera measures depth per pixel, the polarization obtains the degree of polarization, and the panoramic camera captures a 360 • landscape. Data fusion and advanced environment perception could be built upon the combination of sensors. Designed especially for autonomous driving, this vision sensor is shipped with a robust semantic segmentation network. In addition, we demonstrate how cross-modal enhancement could be achieved by registering the color image and the polarization image. An example of water hazard detection is given. To prove the multimodal vision sensor's compatibility with different devices, a brief runtime performance analysis is carried out.

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Section: Discussionmentioning

confidence: 99%

A multimodal vision sensor for autonomous driving

Sun

Huang

Yang

2019

Counterterrorism, Crime Fighting, Forensics, and Surveillance Technologies III

Self Cite

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“…36,37 Unsupervised learning has also been frequently leveraged to pre-process input images, in order to prevent performance from degrading catastrophically when the input domain differs significantly from previously seen domains. 15,38 Specifically, this research line is also highly related to topological localization, 38,39 where modern visual localizers like 40,41 can also benefit from the input adaptation to perform more reliably against variation challenges. More recently, model distillation/imitation were applied to make model behave stable in unseen domains.…”

Section: Model Adaptionmentioning

confidence: 99%

See clearer at night: towards robust nighttime semantic segmentation through day-night image conversion

Sun

Wang

Yang

et al. 2019

Artificial Intelligence and Machine Learning in Defense Applications

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In recent years, intelligent driving navigation and security monitoring have made considerable progress with the help of deep Convolutional Neural Networks (CNNs). As one of the state-of-the-art perception approaches, semantic segmentation unifies distinct detection tasks widely desired by both autonomous driving and security monitoring. Currently, semantic segmentation shows remarkable efficiency and reliability in standard scenarios such as daytime scenes with favorable illumination conditions. However, in face of adverse conditions such as the nighttime, semantic segmentation loses its accuracy significantly. One of the main causes of the problem is the lack of sufficient annotated segmentation datasets of nighttime scenes. In this paper, we propose a framework to alleviate the accuracy decline when semantic segmentation is taken to adverse conditions by using Generative Adversarial Networks (GANs). To bridge the daytime and nighttime image domains, we made key observation that compared to datasets in adverse conditions, there are considerable amount of segmentation datasets in standard conditions such as BDD and our collected ZJU datasets. Our GAN-based nighttime semantic segmentation framework includes two methods. In the first method, GANs were used to translate nighttime images to the daytime, thus semantic segmentation can be performed using robust models already trained on daytime datasets. In another method, we use GANs to translate different ratio of daytime images in the dataset to the nighttime but still with their labels. In this sense, synthetic nighttime segmentation datasets can be generated to yield models prepared to operate at nighttime conditions robustly. In our experiment, the later method significantly boosts the performance at the nighttime evidenced by quantitative results using Intersection over Union (IoU) and Pixel Accuracy (Acc). We show that the performance varies with respect to the proportion of synthetic nighttime images in the dataset, where the sweet spot corresponds to most robust performance across the day and night. The proposed framework not only makes contribution to the optimization of visual perception in intelligent vehicles, but also can be applied to diverse navigational assistance systems.

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

Cited by 25 publications

References 37 publications

A multimodal vision sensor for autonomous driving

A multimodal vision sensor for autonomous driving

See clearer at night: towards robust nighttime semantic segmentation through day-night image conversion

Local Wireless Sensor Networks Positioning Reliability Under Sensor Failure

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