Improving feature-based visual SLAM in Person Indoor Navigation with HDR Imaging

Albrecht, Alexander; Heide, Nina Felicitas

doi:10.1109/icicsp48821.2019.8958583

Cited by 3 publications

(2 citation statements)

References 14 publications

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“…From another point of view, the raw images from the used LWIR camera can be seen as analogous to the images produced by HDR cameras. By changing the parameters of the HDR image processing algorithms, it is possible to extract more confident features from very dark or very bright image regions, which can benefit subsequent computer vision performance [ 30 ].…”

Section: Object Detection For Parametrized Spectrum Imagesmentioning

confidence: 99%

Bias-Tunable Quantum Well Infrared Photodetector

Biswal,

Yakimov,

Tokranov

et al. 2024

Nanomaterials

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With the rapid advancement of Artificial Intelligence-driven object recognition, the development of cognitive tunable imaging sensors has become a critically important field. In this paper, we demonstrate an infrared (IR) sensor with spectral tunability controlled by the applied bias between the long-wave and mid-wave IR spectral regions. The sensor is a Quantum Well Infrared Photodetector (QWIP) containing asymmetrically doped double QWs where the external electric field alters the electron population in the wells and hence spectral responsivity. The design rules are obtained by calculating the electronic transition energies for symmetric and antisymmetric double-QW states using a Schrödinger–Poisson solver. The sensor is grown and characterized aiming detection in mid-wave (~5 µm) to long-wave IR (~8 µm) spectral ranges. The structure is grown using molecular beam epitaxy (MBE) and contains 25 periods of coupled double GaAs QWs and Al0.38Ga0.62As barriers. One of the QWs in the pair is modulation-doped to provide asymmetry in potential. The QWIPs are tested with blackbody radiation and FTIR down to 77 K. As a result, the ratio of the responsivities of the two bands at about 5.5 and 8 µm is controlled over an order of magnitude demonstrating tunability between MWIR and LWIR spectral regions. Separate experiments using parameterized image transformations of wideband LWIR imagery are performed to lay the framework for utilizing tunable QWIP sensors in object recognition applications.

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Section: Object Detection For Parametrized Spectrum Imagesmentioning

confidence: 99%

Bias-Tunable Quantum Well Infrared Photodetector

Biswal,

Yakimov,

Tokranov

et al. 2024

Nanomaterials

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show abstract

“…Through simulation experiment, this proposed framework was not only useful for visual localization but also useful for other advanced tasks of robot. In [17], aiming at the problem of excessive or insufficient exposure of image areas due to the rapidly changing lighting conditions during feature detection, synchronous video stream fusion in HDR stereo cameras can significantly improve the problem of low matching accuracy of identification areas due to changes in light environment and effectively improve the positioning accuracy. The authors in [18][19][20] proposed a localization method based on monocular vision in dynamic environment, aiming at the disturbance of dynamic objects to the estimation results of visual odometer of visual SLAM system in dynamic environment.…”

Section: Related Workmentioning

confidence: 99%

Mutual Positioning Method in Unknown Indoor Environment Based on Visual Image Semantics

Wang

2021

Wireless Communications and Mobile Computing

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In our society, realizing intelligent positioning in indoor environments is important to build a smart city. Currently, mutual positioning requirements in the unknown indoor environment are growing fast. However, in such environment, we can obtain neither outdoor radio signal nor the indoor images in advance for online positioning. Therefore, how to achieve mutual positioning becomes an interesting problem. In this paper, we propose a vision-based mutual positioning method in an unknown indoor environment. First, two users take images of the unknown indoor environment, use semantic segmentation network to identify the semantic targets contained in the images, and upload the generated semantic sequence to the user shared database in real time. Then, every time two users reupload a semantic sequence due to a change of location, it is necessary to retrieve whether another user has uploaded the same semantic sequence in the shared database. If the retrieval is successful, it means that two users have seen the same scene. Finally, two users select a target from the two user images taken based on the same scene to establish a three-dimensional coordinate system, respectively, calculate their own position coordinates in this coordinate system, and realize mutual positioning through position coordinate sharing. Experiment results show that our proposed method can successfully realize mutual positioning between two users in an unknown indoor environment, while ensuring high positioning accuracy.

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Automated camera-exposure control for robust localization in varying illumination environments

Wang

Chen

2022

Auton Robot

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Improving feature-based visual SLAM in Person Indoor Navigation with HDR Imaging

Cited by 3 publications

References 14 publications

Bias-Tunable Quantum Well Infrared Photodetector

Bias-Tunable Quantum Well Infrared Photodetector

Mutual Positioning Method in Unknown Indoor Environment Based on Visual Image Semantics

Automated camera-exposure control for robust localization in varying illumination environments

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