上海交通大学学报

上海交通大学学报 >

2023 , Vol. 57 >Issue 3: 297 - 308

DOI: https://doi.org/10.16183/j.cnki.jsjtu.2021.301

机械与动力工程

基于自适应颜色快速点特征直方图的托盘识别方法

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  • 1.浙江工业大学 机械工程学院, 杭州 310023
    2.诺力智能装备股份有限公司, 浙江 湖州 313199
    3.浙江省智能物流装备工程技术研究中心, 浙江 湖州 313199
詹 燕(1976-),博士,副教授,研究方向为智能物流、系统优化.

收稿日期: 2021-08-12

  录用日期: 2021-10-08

  网络出版日期: 2022-08-23

基金资助

浙江省自然科学基金项目(LQ22E050017);中国博士后科学基金项目(2021M702894);浙江省博士后科研择优资助项目(ZJ2021119);浙江省重点研发计划资助项目(2018C01003)

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A Pallet Recognition Method Based on Adaptive Color Fast Point Feature Histogram

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  • 1. College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou 310023, China
    2. Noblelift Intelligent Equipment Co., Ltd., Huzhou 313199, Zhejiang, China
    3. Engineering Research Center of Intelligent Logistics Equipment of Zhejiang Province, Huzhou 313199, Zhejiang, China

Received date: 2021-08-12

  Accepted date: 2021-10-08

  Online published: 2022-08-23

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摘要

托盘识别是无人驾驶工业车辆进行货物搬运的关键技术之一.针对现有托盘识别方法低效耗时、鲁棒性差、参数选择随意的缺点,提出了一种基于自适应颜色快速点特征直方图的托盘识别方法.该方法使用Kinect V2传感器采集包含托盘的场景点云数据,点云经离群点剔除后,基于邻域特征熵函数最小准则获取每个点的最优邻域半径.提取点云关键点,计算关键点的颜色特征和自适应邻域快速点特征直方图,融合成自适应颜色快速点特征直方图,进行特征匹配与误匹配点对剔除,从而实现托盘识别.与固定邻域半径为0.012 m的快速点特征直方图对比,实验结果表明:基于自适应颜色快速点特征直方图的托盘识别精度提高了83.74%,特征提取用时减少了35.55%,验证了方法的优越性.

关键词： 快速点特征直方图; 特征提取; 自适应最优邻域; 托盘识别

本文引用格式

詹燕, 陈志慧, 朱宝昌, 朱婷婷, 邵益平, 鲁建厦 . 基于自适应颜色快速点特征直方图的托盘识别方法[J]. 上海交通大学学报, 2023 , 57(3) : 297 -308 . DOI: 10.16183/j.cnki.jsjtu.2021.301

Abstract

Pallet recognition is one of the critical technologies of cargo handling for unmanned industrial vehicles. A pallet recognition method based on adaptive color fast point feature histogram (ACFPFH) is proposed to solve the problems of current recognition methods such as low efficiency, time-consuming, poor robustness and random parameter selection. The Kinect V2 sensor is used to collect the point cloud data which represents the whole scene including pallet. Next, outliers are removed and the optimal neighborhood radius of each point is obtained based on the minimum criterion of neighborhood feature entropy function. Then, the key points are extracted from scene point clouds. The ACFPFH consisting of color feature and adaptive geometric feature is applied for similarity matching between the template and scene point clouds. Finally, wrong feature correspondences are rejected and the pallet in the scene point cloud is recognized. A comparison of the fast point feature histogram with the fixed neighbor radius of 0.012 m shows that the pallet recognition precision and efficiency of the method based on ACFPFH is improved by 83.74% and 35.55% respectively, which verifies the superiority of the proposed method.

Key words： fast point feature histogram; feature extraction; adaptive optimal neighborhood; pallet recognition

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