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 an 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 https://youtu.be/_cLCmkW4UYo. To encourage further research, we will share a chargepad dataset used in this work.
翻译:电动车辆越来越普遍,电动充电器被视作一种方便而有效的电动车辆充电装置。然而,驾驶员通常很难使车辆与高效电动充电装置的必要准确性相匹配,使得两个充电牌的自动对齐成为可取的。在车辆机队电气化的同时,使用环形摄像系统的自动停车系统越来越受欢迎。在这项工作中,我们提议以环形摄像结构为基础的系统来检测、本地化和自动调整车辆与充电装置。充电板的视觉设计不是标准化的,也不一定事先知道。因此,依赖离线培训的系统在某些情况下会失败。因此,我们提议一种在线学习方法,在手动使车辆与充电装置相匹配时,利用司机行动的手段,并结合了来自语管路段的薄弱监督力和深度,以便学习一个解析器,在视频上自动识别充电板,供进一步培训。这样,当我们面对前看不见充电板时,充电板的视觉设计,司机只需要手动调整车辆的相对电路路段。我们用一个平坦的电路路段来测量。