相关数据集
Charging port point-cloud recognition and positioning method based on density clustering and geometric features
随着新能源电动汽车的普及和市场规模的不断扩大，电动汽车的自动充电问题逐渐成为研究热点。目前，在充电机器人领域有丰富的研究成果，其中一些已经能够实现自主充电。但是，在与充电口自动对接时，核心充电口的识别和定位存在准确率低、精度不足、计算复杂等问题，说明目前的充电口识别和定位方法存在一定的局限性。为了提高充电口识别和定位的效率和准确性，本文提出了一种新方法：基于基于密度的噪声应用空间聚类（DBSCAN
Figshare2024-11-19 更新90
Electric Vehicle Charging Port Recognition and Positioning Algorithm Based on Point Cloud
This study introduces an automatic identification and positioning method for electric vehicle charging ports based on 3D point cloud key-point features. Compared to traditional image processing and de
Figshare2024-08-04 更新60
Electric Vehicle Charging Port Rec ognition and Positioning Algorithm Based on Point Cloud（ID: PONE-D-24-13766R 1 ）
This study introduces an automatic identification and positioning method for electric vehicle charging ports based on 3D point cloud key-point features. Compared to traditional image processing and de
Figshare2024-08-04 更新00
measurement value and part of the point cloud information
通过操作机器人对接充电口进行实验，以验证算法的结果。
Figshare2024-11-19 更新80
(ID: PONE-D-24-13766R 1) Electric Vehicle Charging Port Rec ognition and Positioning Algorithm Based on Point Cloud
This study introduces an automatic identification and positioning method for electric vehicle charging ports based on 3D point cloud key-point features. Compared to traditional image processing and de
Figshare2024-11-18 更新10
