An Online Learning System for Wireless Charging Alignment Using Surround-View Fisheye Cameras

Dahal, Ashok; Kumar, Varun; Yogamani, Senthil; Eising, Ciaran

doi:10.1109/tits.2022.3182165

Cited by 5 publications

(1 citation statement)

References 39 publications

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“…The usage of surround-view cameras is ideal as it's a near-field perception task and implemented as an extension of the automated parking system. Dahal et al [107] proposes a methodology premised on a surround-view camera framework that automatically identifies, localizes, and aligns the vehicle with the inductive chargepad. The visual design of the chargepads is not consistent and is often not recognized ahead of time.…”

Section: A Semantic Tasksmentioning

confidence: 99%

Surround-View Fisheye Camera Perception for Automated Driving: Overview, Survey & Challenges

Kumar

Eising

Witt

et al. 2023

IEEE Trans. Intell. Transport. Syst.

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Surround-view fisheye cameras are commonly used for near-field sensing in automated driving. Four fisheye cameras on four sides of the vehicle are sufficient to cover 360 • around the vehicle capturing the entire near-field region. Some primary use cases are automated parking, traffic jam assist, and urban driving. There are limited datasets and very little work on near-field perception tasks as the focus in automotive perception is on far-field perception. In contrast to far-field, surround-view perception poses additional challenges due to high precision object detection requirements of 10cm and partial visibility of objects. Due to the large radial distortion of fisheye cameras, standard algorithms cannot be extended easily to the surround-view use case. Thus, we are motivated to provide a self-contained reference for automotive fisheye camera perception for researchers and practitioners. Firstly, we provide a unified and taxonomic treatment of commonly used fisheye camera models. Secondly, we discuss various perception tasks and existing literature. Finally, we discuss the challenges and future direction.

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Section: A Semantic Tasksmentioning

confidence: 99%