Automated valet parking and charging for e-mobility

Schwesinger, Ulrich; Bürki, Mathias; Timpner, Julian; Rottmann, Stephan; Wolf, Lars; Paz, Lina Maria; Grimmett, Hugo; Posner, Ingmar; Newman, Paul; Häne, Christian; Heng, Lionel; Lee, Gim Hee; Sattler, Torsten; Pollefeys, Marc; Allodi, Marco; Valenti, Francesco; Mimura, Kazuya; Goebelsmann, Bernd; Derendarz, Wojciech; Mühlfellner, Peter; Wonneberger, Stefan; Waldmann, Rene; Grysczyk, Sebastian; Bruning, Stefan; Horstmann, S.; Bartholomaus, Marc; Brummer, Clemens; Stellmacher, Martin; Pucks, Fabian; Nicklas, Marcel; Siegwart, Roland

doi:10.1109/ivs.2016.7535380

Cited by 56 publications

(31 citation statements)

References 29 publications

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“…In [Ulrich & al, 2016] & [Kyoung-Wook Min & Jeong-Dan Choi, 2015] an intelligent system of self-parking vehicle is proposed for low speed limited parking area. Each vehicle in this approach need to be equipped with several cameras for localization, two LIDAR sensors for forward obstacle detection and several sonar sensors for detecting near obstacles.…”

Section: Literatures Reviewmentioning

confidence: 99%

“… Fully automated parking (mechanical) [3], [4]: the whole parking operation is performed by the parking system; their major inconvenient is the implementation cost.  Self-parking car [1][2]5]: Cars are equipped with sophisticated systems that can perform the full parking operation autonomously. The use of such car is very limited due to their high cost.…”

Section: Literatures Reviewmentioning

confidence: 99%

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New approach for self-parking management system

Lahdili

Ismaili

2018

IJET

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The parking operation is considered the most stressful task amongst all driving experiences, and during this operation a considerable number of accidents are occurring each day. As a result, Parking Assistance Solutions (PAS) are widely used nowadays. Unfortunately, those systems are not fully automated and their role is limited to assisting the driver during the parking operation. This paper presents a proposal of a low cost, fully automated self-parking management solution. This proposal is a combination of two subsystems that will be collaborating to achieve a fully automated system; the first part is composed of an indoor navigation system, based on the adaptation of the available surveillance camera network in the parking area. This system will locate vehicles in the parking area and provide their current position in real time. And based on the localization information in addition to available free parking lots a server will generate navigation consigns. Those consigns (speed, rotation angle and direction) will be used by an auto-parking kit (that represent the second part of our proposal) to control the parking vehicle, that will drive itself to its reserved place.

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Section: Literatures Reviewmentioning

confidence: 99%

Section: Literatures Reviewmentioning

confidence: 99%

New approach for self-parking management system

Lahdili

Ismaili

2018

IJET

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“…Project AutoVision started in late 2016 with the goal to develop localization and 3D scene perception algorithms 1 DSO National Laboratories 2 ETH Zürich 3 National University of Singapore 4 MPI-IS and University of Tübingen 5 Microsoft, Switzerland 6 Chalmers University of Technology, Sweden for autonomous vehicles exclusively equipped with cameras. Project AutoVision is similar to, but differs from Project V-Charge [9,31,14] in the aspect that Project AutoVision extends the operating envelope from parking lots and garages to large-scale urban and rural environments with higher driving speeds and widely varying illumination conditions. Parallels can also be drawn between Project AutoVision and AutoX, both of which only rely on cameras for localization and perception.…”

Section: Introductionmentioning

confidence: 99%

Project AutoVision: Localization and 3D Scene Perception for an Autonomous Vehicle with a Multi-Camera System

Heng

Choi

Cui

et al. 2019

2019 International Conference on Robotics and Automation (ICRA)

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101

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Project AutoVision aims to develop localization and 3D scene perception capabilities for a self-driving vehicle. Such capabilities will enable autonomous navigation in urban and rural environments, in day and night, and with cameras as the only exteroceptive sensors. The sensor suite employs many cameras for both 360-degree coverage and accurate multi-view stereo; the use of low-cost cameras keeps the cost of this sensor suite to a minimum. In addition, the project seeks to extend the operating envelope to include GNSS-less conditions which are typical for environments with tall buildings, foliage, and tunnels. Emphasis is placed on leveraging multi-view geometry and deep learning to enable the vehicle to localize and perceive in 3D space. This paper presents an overview of the project, and describes the sensor suite and current progress in the areas of calibration, localization, and perception.

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“…Recently, localization with visual sensors such as cameras has attracted a lot of interest, which is often called visual odometry (VO), or visual simultaneous localization and mapping (VSLAM) for a more comprehensive system. Although it is still a long way to design a universal VO system suitable for all applications, huge progress has been achieved on deploying the system to some specific scenarios, for example, to provide an autonomous parking service in a parking lot [1]. To acquire an accurate and robust performance, the vision system mounted a on vehicle usually consists of multiple high-resolution cameras with different field of view (FoV).…”

Section: Introductionmentioning

confidence: 99%

ViLiVO: Virtual LiDAR-Visual Odometry for an Autonomous Vehicle with a Multi-Camera System

Xiang

et al. 2019

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

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In this paper, we present a multi-camera visual odometry (VO) system for an autonomous vehicle. Our system mainly consists of a virtual LiDAR and a pose tracker. We use a perspective transformation method to synthesize a surroundview image from undistorted fisheye camera images. With a semantic segmentation model, the free space can be extracted. The scans of the virtual LiDAR are generated by discretizing the contours of the free space. As for the pose tracker, we propose a visual odometry system fusing both the feature matching and the virtual LiDAR scan matching results. Only those feature points located in the free space area are utilized to ensure the 2D-2D matching for pose estimation. Furthermore, bundle adjustment (BA) is performed to minimize the feature points reprojection error and scan matching error. We apply our system to an autonomous vehicle equipped with four fisheye cameras. The testing scenarios include an outdoor parking lot as well as an indoor garage. Experimental results demonstrate that our system achieves a more robust and accurate performance comparing with a fisheye camera based monocular visual odometry system.

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Automated valet parking and charging for e-mobility

Cited by 56 publications

References 29 publications

New approach for self-parking management system

New approach for self-parking management system

Project AutoVision: Localization and 3D Scene Perception for an Autonomous Vehicle with a Multi-Camera System

ViLiVO: Virtual LiDAR-Visual Odometry for an Autonomous Vehicle with a Multi-Camera System

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