# An Online Learning System for Wireless Charging Alignment using Surround-view Fisheye Cameras arXiv: https://arxiv.org/abs/2105.12763 ยท HTML: https://mlsanity.com/paper/2105.12763/ ## Key facts - Cited by 9 papers in the map (data through 14 Oct 2026). - Builds on 28 of its 73 references that are in the map. - First posted on arXiv 26 May 2021. ## Builds on (most cited first, 28 of 28) - [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015) - [YOLOv3: An Incremental Improvement](https://mlsanity.com/paper/1804.02767/) (2018) - [Unsupervised Representation Learning by Predicting Image Rotations](https://mlsanity.com/paper/1803.07728/) (2018) - [ORB-SLAM: a Versatile and Accurate Monocular SLAM System](https://mlsanity.com/paper/1502.00956/) (2015) - [On the Variance of the Adaptive Learning Rate and Beyond](https://mlsanity.com/paper/1908.03265/) (2019) - [Albumentations: fast and flexible image augmentations](https://mlsanity.com/paper/1809.06839/) (2018) - [SCAN: Learning to Classify Images without Labels](https://mlsanity.com/paper/2005.12320/) (2020) - [Lookahead Optimizer: k steps forward, 1 step back](https://mlsanity.com/paper/1907.08610/) (2019) - [S4L: Self-Supervised Semi-Supervised Learning](https://mlsanity.com/paper/1905.03670/) (2019) - [Orthographic Feature Transform for Monocular 3D Object Detection](https://mlsanity.com/paper/1811.08188/) (2018) - [WoodScape: A multi-task, multi-camera fisheye dataset for autonomous driving](https://mlsanity.com/paper/1905.01489/) (2019) - [Feature-metric Loss for Self-supervised Learning of Depth and Egomotion](https://mlsanity.com/paper/2007.10603/) (2020) - [OmniDet: Surround View Cameras based Multi-task Visual Perception Network for Autonomous Driving](https://mlsanity.com/paper/2102.07448/) (2021) - [SynDistNet: Self-Supervised Monocular Fisheye Camera Distance Estimation Synergized with Semantic Segmentation for Autonomous Driving](https://mlsanity.com/paper/2008.04017/) (2020) - [Generalized Object Detection on Fisheye Cameras for Autonomous Driving: Dataset, Representations and Baseline](https://mlsanity.com/paper/2012.02124/) (2020) - [FisheyeDistanceNet: Self-Supervised Scale-Aware Distance Estimation using Monocular Fisheye Camera for Autonomous Driving](https://mlsanity.com/paper/1910.04076/) (2019) - [Inspect, Understand, Overcome: A Survey of Practical Methods for AI Safety](https://mlsanity.com/paper/2104.14235/) (2021) - [UnRectDepthNet: Self-Supervised Monocular Depth Estimation using a Generic Framework for Handling Common Camera Distortion Models](https://mlsanity.com/paper/2007.06676/) (2020) - [SVDistNet: Self-Supervised Near-Field Distance Estimation on Surround View Fisheye Cameras](https://mlsanity.com/paper/2104.04420/) (2021) - [Near-field Perception for Low-Speed Vehicle Automation using Surround-view Fisheye Cameras](https://mlsanity.com/paper/2103.17001/) (2021) - [Weather and Light Level Classification for Autonomous Driving: Dataset, Baseline and Active Learning](https://mlsanity.com/paper/2104.14042/) (2021) - [Monocular Fisheye Camera Depth Estimation Using Sparse LiDAR Supervision](https://mlsanity.com/paper/1803.06192/) (2018) - [FisheyeMODNet: Moving Object detection on Surround-view Cameras for Autonomous Driving](https://mlsanity.com/paper/1908.11789/) (2019) - [TiledSoilingNet: Tile-level Soiling Detection on Automotive Surround-view Cameras Using Coverage Metric](https://mlsanity.com/paper/2007.00801/) (2020) - [Trained Trajectory based Automated Parking System using Visual SLAM on Surround View Cameras](https://mlsanity.com/paper/2001.02161/) (2020) - [NeurAll: Towards a Unified Visual Perception Model for Automated Driving](https://mlsanity.com/paper/1902.03589/) (2019) - [Computer vision in automated parking systems: Design, implementation and challenges](https://mlsanity.com/paper/2104.12537/) (2021) - [Recent Advances in Features Extraction and Description Algorithms: A Comprehensive Survey](https://mlsanity.com/paper/1703.06376/) (2017) ## Built on by (most cited first, 9 of 9) - [Surround-view Fisheye Camera Perception for Automated Driving: Overview, Survey and Challenges](https://mlsanity.com/paper/2205.13281/) (2022) - [SynWoodScape: Synthetic Surround-view Fisheye Camera Dataset for Autonomous Driving](https://mlsanity.com/paper/2203.05056/) (2022) - [LiMoSeg: Real-time Bird's Eye View based LiDAR Motion Segmentation](https://mlsanity.com/paper/2111.04875/) (2021) - [Adversarial Attacks on Multi-task Visual Perception for Autonomous Driving](https://mlsanity.com/paper/2107.07449/) (2021) - [Woodscape Fisheye Object Detection for Autonomous Driving -- CVPR 2022 OmniCV Workshop Challenge](https://mlsanity.com/paper/2206.12912/) (2022) - [Surround-View Cameras based Holistic Visual Perception for Automated Driving](https://mlsanity.com/paper/2206.05542/) (2022) - [SpikiLi: A Spiking Simulation of LiDAR based Real-time Object Detection for Autonomous Driving](https://mlsanity.com/paper/2206.02876/) (2022) - [UnShadowNet: Illumination Critic Guided Contrastive Learning For Shadow Removal](https://mlsanity.com/paper/2203.15441/) (2022) - [WoodScape Motion Segmentation for Autonomous Driving -- CVPR 2023 OmniCV Workshop Challenge](https://mlsanity.com/paper/2401.00910/) (2023) ## Abstract (arXiv) Electric Vehicles are increasingly common, with inductive chargepads being considered a convenient and efficient means of charging electric vehicles. However, drivers are typically poor at aligning the vehicle to the necessary accuracy for efficient inductive charging, making the automated alignment of the two charging plates desirable. In parallel to the electrification of the vehicular fleet, automated parking systems that make use of surround-view camera systems are becoming increasingly popular. In this work, we propose a system based on the surround-view camera architecture to detect, localize, and automatically align the vehicle with the inductive chargepad. The visual design of the chargepads is not standardized and not necessarily known beforehand. Therefore, a system that relies on offline training will fail in some situations. Thus, we propose a self-supervised online learning method that leverages the driver's actions when manually aligning the vehicle with the chargepad and combine it with weak supervision from semantic segmentation and depth to learn a classifier to auto-annotate the chargepad in the video for further training. In this way, when faced with a previously unseen chargepad, the driver needs only manually align the vehicle a single time. As the chargepad is flat on the ground, it is not easy to detect it from a distance. Thus, we propose using a Visual SLAM pipeline to learn landmarks relative to the chargepad to enable alignment from a greater range. We demonstrate the working system on an automated vehicle as illustrated in the video at https://youtu.be/_cLCmkW4UYo. To encourage further research, we will share a chargepad dataset used in this work.