在ICRA感知挑战11解决方案的背景下,雅各布斯机器人技术对物体识别和定位的方法

N. Vaskevicius, K. Pathak, A. Ichim, A. Birk
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引用次数: 26

摘要

在本文中,我们概述了Jacobs机器人公司在ICRA“11个感知挑战解决方案”中的参赛情况。我们提出了基于Kinect传感器提供的综合几何和视觉信息的多管齐下的物体识别和定位策略。首先,使用边缘检测算法对距离图像进行过度分割,并基于每个片段的简单形状分析提取感兴趣的区域。然后,利用视觉特征及其在三维空间中的分布,将这些选定的场景区域与已知物体进行匹配。最后,通过使用估计的变换和传感器模型将学习到的3D模型反向投影到场景中,对生成的关于物体位置的假设进行测试。我们的方法在八种竞争算法中获得了第二名,只略微输给了获胜者。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The jacobs robotics approach to object recognition and localization in the context of the ICRA'11 Solutions in Perception Challenge
In this paper, we give an overview of the Jacobs Robotics entry to the ICRA'11 Solutions in Perception Challenge. We present our multi-pronged strategy for object recognition and localization based on the integrated geometric and visual information available from the Kinect sensor. Firstly, the range image is over-segmented using an edge-detection algorithm and regions of interest are extracted based on a simple shape-analysis per segment. Then, these selected regions of the scene are matched with known objects using visual features and their distribution in 3D space. Finally, generated hypotheses about the positions of the objects are tested by back-projecting learned 3D models to the scene using estimated transformations and sensor model. Our method won the second place among eight competing algorithms, only marginally losing to the winner.
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