Visual 6D pose estimation of battery package locking mechanism in intelligent battery swapping station

WANG Lihui; SU Yuzuwei; HAN Chunhua; CHEN Liangliang; ZHANG Hao

doi:10.37188/OPE.20233121.3135

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Visual 6D pose estimation of battery package locking mechanism in intelligent battery swapping station

Information Sciences | 更新时间：2023-11-16

- Visual 6D pose estimation of battery package locking mechanism in intelligent battery swapping station
- Optics and Precision Engineering Vol. 31, Issue 21, Pages: 3135-3144(2023)
- 作者机构：
  
  1.东南大学仪器科学与工程学院微惯性仪表与先进导航技术教育部重点实验室，江苏南京 210096
  2.国网江苏省电力有限公司，江苏南京210024
  3.国电南瑞科技股份有限公司，江苏南京211100
- 作者简介：
- 基金信息：
- DOI：10.37188/OPE.20233121.3135
  CLC：
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王立辉,苏余足威,韩华春等.智能换电站电池包锁止机构位姿视觉估计[J].光学精密工程,2023,31(21):3135-3144.

WANG Lihui,SU Yuzuwei,HAN Chunhua,et al.Visual 6D pose estimation of battery package locking mechanism in intelligent battery swapping station[J].Optics and Precision Engineering,2023,31(21):3135-3144.
王立辉,苏余足威,韩华春等.智能换电站电池包锁止机构位姿视觉估计[J].光学精密工程,2023,31(21):3135-3144. DOI： 10.37188/OPE.20233121.3135.

WANG Lihui,SU Yuzuwei,HAN Chunhua,et al.Visual 6D pose estimation of battery package locking mechanism in intelligent battery swapping station[J].Optics and Precision Engineering,2023,31(21):3135-3144. DOI： 10.37188/OPE.20233121.3135.

摘要

面向电动汽车的电池换电需求，对换电站电池包对接中的锁止机构定位问题，提出了一种基于点云分割的电池包锁止机构6D位姿估计方法。该方法使用YOLOv5网络从场景中分割出锁止机构的点云，并使用体素滤波与移动最小二乘拟合进行点云的滤波与平滑；通过引入点云分割网络预测点云标签，为快速点特征直方图特征加入全局语义特征，弥补快速点特征直方图只有点云局部特征的缺陷，并根据该特征进行随机抽样一致性刚体点云配准，估计锁止机构点云的6D位姿，最后使用迭代最近点方法算法校正位姿估计结果。实验结果表明，基于点云分割的锁止机构6D位姿估计算法精度较高，可以克服环境噪声导致的误匹配，精确获取锁止机构位姿，其位姿估计的角度误差可以达到1.90°，位移误差可以达到1.4 mm，RMSE可以达到1.5 mm，为换电站电池对接定位提供了有效的解决途径。

Abstract

In order to meet the battery replacement demand of electric vehicles， a 6D pose estimation method of battery package locking mechanism based on point cloud segmentation is proposed to solve the positioning problem of locking mechanism during battery package docking in battery swapping station. This method uses YOLOv5 network to segment the point cloud of locking mechanism from the scene， and uses voxel filtering and moving least square fitting to filter and smooth the point cloud. The point cloud labels are predicted by the point cloud segmentation network， and the global semantic feature is added to the Fast Point Feature Histograms （FPFH） feature to make up for the defect that the FPFH has only the local feature of the point cloud. According to this feature， the Random Sample Consensus （RANSAC） rigid point cloud registration is leveraged， and the 6D pose of the locking mechanism point cloud is estimated. Finally， the Iterative Closest Point（ICP） algorithm is used to correct the pose estimation results. The experimental results show that the 6D pose estimation algorithm of locking mechanism based on point cloud segmentation has high accuracy， and can overcome the mismatching caused by environmental noise， and accurately obtain the position and attitude of locking mechanism. The angle error of position and attitude estimation can reach 1.90°， the displacement error can reach 1.4 mm， and the RMSE can reach 1.5 mm， which provides an effective solution for battery docking positioning in battery swapping station.

关键词

换电机器人电池包位姿估计点云配准点云分割

Keywords

battery swapping robotbattery packpose estimationpoint cloud registrationpoint cloud segmentation

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