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Electric Vehicles are increasingly common, with inductive chargepads beingconsidered a convenient and efficient means of charging electric vehicles.However, drivers are typically poor at aligning the vehicle to the necessaryaccuracy for efficient inductive charging, making the automated alignment ofthe two charging plates desirable. In parallel to the electrification of thevehicular fleet, automated parking systems that make use of surround-viewcamera systems are becoming increasingly popular. In this work, we propose asystem based on the surround-view camera architecture to detect, localize, andautomatically align the vehicle with the inductive chargepad. The visual designof the chargepads is not standardized and not necessarily known beforehand.Therefore, a system that relies on offline training will fail in somesituations. Thus, we propose a self-supervised online learning method thatleverages the driver's actions when manually aligning the vehicle with thechargepad and combine it with weak supervision from semantic segmentation anddepth to learn a classifier to auto-annotate the chargepad in the video forfurther training. In this way, when faced with a previously unseen chargepad,the driver needs only manually align the vehicle a single time. As thechargepad 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 tothe chargepad to enable alignment from a greater range. We demonstrate theworking system on an automated vehicle as illustrated in the video athttps://youtu.be/_cLCmkW4UYo. To encourage further research, we will share achargepad dataset used in this work.

An Online Learning System for Wireless Charging Alignment using Surround-view Fisheye Cameras