  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.
