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首页> 外文期刊>IEEE Robotics & Automation Magazine >Object Detection and Recognition for Assistive Robots: Experimentation and Implementation
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Object Detection and Recognition for Assistive Robots: Experimentation and Implementation

机译：辅助机器人的目标检测与识别：实验与实现

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摘要

Technological advances are being made to assist humans in performing ordinary tasks in everyday settings. A key issue is the interaction with objects of varying size, shape, and degree of mobility. Autonomous assistive robots must be provided with the ability to process visual data in real time so that they can react adequately for quickly adapting to changes in the environment. Reliable object detection and recognition is usually a necessary early step to achieve this goal. In spite of significant research achievements, this issue still remains a challenge when real-life scenarios are considered. In this article, we present a vision system for assistive robots that is able to detect and recognize objects from a visual input in ordinary environments in real time. The system computes color, motion, and shape cues, combining them in a probabilistic manner to accurately achieve object detection and recognition, taking some inspiration from vision science. In addition, with the purpose of processing the input visual data in real time, a graphical processing unit (GPU) has been employed. The presented approach has been implemented and evaluated on a humanoid robot torso located at realistic scenarios. For further experimental validation, a public image repository for object recognition has been used, allowing a quantitative comparison with respect to other state-of-the-art techniques when realworld scenes are considered. Finally, a temporal analysis of the performance is provided with respect to image resolution and the number of target objects in the scene.
机译：技术进步正在帮助人们在日常环境中执行普通任务。一个关键问题是与大小，形状和活动度不同的对象的交互作用。自主辅助机器人必须具有实时处理视觉数据的能力，以便它们能够做出适当反应，以快速适应环境变化。可靠的对象检测和识别通常是实现此目标的必要的早期步骤。尽管取得了重大的研究成就，但考虑到现实生活中的情况，这个问题仍然是一个挑战。在本文中，我们介绍了一种用于辅助机器人的视觉系统，该系统能够在普通环境中实时地从视觉输入中检测和识别对象。该系统计算颜色，运动和形状提示，以概率方式将它们组合在一起，以准确实现对象检测和识别，并从视觉科学中获得一些启发。另外，出于实时处理输入视觉数据的目的，已经采用了图形处理单元（GPU）。所提出的方法已经在现实情况下的人形机器人躯干上实施和评估。为了进行进一步的实验验证，已经使用了用于对象识别的公共图像存储库，可以在考虑现实场景时与其他最新技术进行定量比较。最后，就图像分辨率和场景中目标对象的数量提供了性能的时间分析。

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