计算机科学
稳健性(进化)
人工智能
杂乱
姿势
计算机视觉
模式识别(心理学)
特征(语言学)
分拆(数论)
目标检测
投票
数学
政治
基因
政治学
电信
哲学
生物化学
化学
法学
雷达
语言学
组合数学
作者
Guokang Wang,Lei Yang,Yanhong Liu
出处
期刊:Chinese Control and Decision Conference
日期:2020-08-01
卷期号:: 455-460
被引量:4
标识
DOI:10.1109/ccdc49329.2020.9164326
摘要
3D-Object detection and pose estimation of free-form rigid objects is a major part in manipulation tasks. Recently, the feature-based method Hs-PPF (point pair feature method proposed by Hinterstoisser) has shown a promising result with high recall rate and strong robustness against sensor noise and clutter. On this line, we propose here a new voting scheme with a series of improvements including scene points indexes disruption, scene points partition and supporting points removal strategies, which significantly reduces the computational time. The experiments on public available dataset demonstrate that our approach, with reasonable configuration, is much faster than Hs-PPF without significant sacrifice of recognition performance.
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