Trained Trajectory based Automated Parking System using Visual SLAM on Surround View Cameras

Tripathi, Nivedita; Yogamani, Senthil

doi:10.48550/arxiv.2001.02161

Cited by 3 publications

(3 citation statements)

References 15 publications

(15 reference statements)

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“…is now also critical. For 3D vision, such as real-time localization and map construction (SLAM) [5], augmented reality (AR) [6], robot navigation [7], artificial vision-based pose, and position estimation systems are very important. Distance estimation is one of these fundamental problems and has promising applications in many fields.…”

Section: Introductionmentioning

confidence: 99%

Self-Supervised Object Distance Estimation Using a Monocular Camera

Hong

Zhang

2022

Sensors

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Distance estimation using a monocular camera is one of the most classic tasks for computer vision. Current monocular distance estimating methods need a lot of data collection or they produce imprecise results. In this paper, we propose a network for both object detection and distance estimation. A network-based on ShuffleNet and YOLO is used to detect an object, and a self-supervised learning network is used to estimate distance. We calibrated the camera, and the calibrated parameters were integrated into the overall network. We also analyzed the parameter variation of the camera pose. Further, a multi-scale resolution is applied to improve estimation accuracy by enriching the expression ability of depth information. We validated the results of object detection and distance estimation on the KITTI dataset and demonstrated that our approach is efficient and accurate. Finally, we construct a dataset and conduct similar experiments to verify the generality of the network in other scenarios. The results show that our proposed methods outperform alternative approaches on object-specific distance estimation.

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Section: Introductionmentioning

confidence: 99%

Self-Supervised Object Distance Estimation Using a Monocular Camera

Hong

Zhang

2022

Sensors

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“…An alternative approach is to work directly in the fisheye image space and thereby avoid such issues. Recently, there has been progress with such approaches for various visual perception tasks such as dense matching [6], object detection [7], depth estimation [8], re-localisation [9] and soiling detection [10].…”

Section: Introductionmentioning

confidence: 99%

FisheyeSuperPoint: Keypoint Detection and Description Network for Fisheye Images

Konrad¹,

Eising²,

Sistu³

et al. 2021

Preprint

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Keypoint detection and description is a commonly used building block in computer vision systems particularly for robotics and autonomous driving. Recently CNN based approaches have surpassed classical methods in a number of perception tasks. However, the majority of techniques to date have focused on standard cameras with little consideration given to fisheye cameras which are commonly used in autonomous driving. In this paper, we propose a novel training and evaluation pipeline for fisheye images. We make use of SuperPoint as our baseline which is a self-supervised keypoint detector and descriptor that has achieved state-of-the-art results on homography estimation. We introduce a fisheye adaptation pipeline to enable training on undistorted fisheye images. We evaluate the performance on the HPatches benchmark, and, by introducing a fisheye based evaluation methods for detection repeatability and descriptor matching correctness, on the Oxford RobotCar datasets.

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“…Especially cameras, provide a lot of useful information while being relatively cheap. There is extensive progress in various visual perception tasks such as semantic segmentation [2], moving object detection [3], depth estimation [4], re-localisation [5] and fusion [6], [7]. Due to the maturity of these visual perception algorithms, there is increasing focus on difficult conditions due to adverse weather conditions such as snow, rain, fog, etc.…”

Section: Introductionmentioning

confidence: 99%

TiledSoilingNet: Tile-level Soiling Detection on Automotive Surround-view Cameras Using Coverage Metric

Das¹,

Křížek²,

Sistu³

et al. 2020

Preprint

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Automotive cameras, particularly surround-view cameras, tend to get soiled by mud, water, snow, etc. For higher levels of autonomous driving, it is necessary to have a soiling detection algorithm which will trigger an automatic cleaning system. Localized detection of soiling in an image is necessary to control the cleaning system. It is also necessary to enable partial functionality in unsoiled areas while reducing confidence in soiled areas. Although this can be solved using a semantic segmentation task, we explore a more efficient solution targeting deployment in low power embedded system. We propose a novel method to regress the area of each soiling type within a tile directly. We refer to this as coverage. The proposed approach is better than learning the dominant class in a tile as multiple soiling types occur within a tile commonly. It also has the advantage of dealing with coarse polygon annotation, which will cause the segmentation task. The proposed soiling coverage decoder is an order of magnitude faster than an equivalent segmentation decoder. We also integrated it into an object detection and semantic segmentation multi-task model using an asynchronous back-propagation algorithm. A portion of the dataset used will be released publicly as part of our WoodScape dataset [1] to encourage further research.

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Trained Trajectory based Automated Parking System using Visual SLAM on Surround View Cameras

Cited by 3 publications

References 15 publications

Self-Supervised Object Distance Estimation Using a Monocular Camera

Self-Supervised Object Distance Estimation Using a Monocular Camera

FisheyeSuperPoint: Keypoint Detection and Description Network for Fisheye Images

TiledSoilingNet: Tile-level Soiling Detection on Automotive Surround-view Cameras Using Coverage Metric

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