PERCH: Perception via search for multi-object recognition and localization
PERCH: Perception via search for multi-object recognition and localization
复制标题
PERCH：通过搜索进行多目标识别和定位的感知
DOI：
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发表时间：
2015
期刊：
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通讯作者：
M. Likhachev
中科院分区：
文献类型：
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作者：
V. Narayanan;M. Likhachev
在许多机器人领域，例如灵活的自动化制造或个人帮助，基本的感知任务是识别和本地化对象的3D模型。该问题的规范方法包括歧视方法，这些方法可以找到在模型和观察到的数据上计算的特征描述符之间的对应关系。尽管这些方法已成功地使用了，但是当特征描述符未能捕获观察到的数据中的变化时，它们可能是不可靠的。一个经典的原因是阻塞。作为迈向审议推理的一步，我们提出了Perch：通过搜索感知，该算法试图通过以生成的方式假设可能的场景来找到对观察到的传感器数据的最佳解释。我们的贡献是：i）将多对象识别和本地化任务制定为在假设场景的空间上的优化问题，ii）在优化中利用结构来将其作为组合搜索问题，以在我们所说的单调场景生成树上，以及iii）利用并行化以及多功能搜索的最新进展，使组合搜索可牵引。我们证明，我们的系统可以保证在所选成本功能下对场景产生最佳解释，并验证我们对现实世界RGB-D测试数据的主张。我们的实验结果表明，我们可以识别和定位在重闭塞的情况下 - 最先进方法挣扎的情况。
In many robotic domains such as flexible automated manufacturing or personal assistance, a fundamental perception task is that of identifying and localizing objects whose 3D models are known. Canonical approaches to this problem include discriminative methods that find correspondences between feature descriptors computed over the model and observed data. While these methods have been employed successfully, they can be unreliable when the feature descriptors fail to capture variations in observed data; a classic cause being occlusion. As a step towards deliberative reasoning, we present PERCH: PErception via SeaRCH, an algorithm that seeks to find the best explanation of the observed sensor data by hypothesizing possible scenes in a generative fashion. Our contributions are: i) formulating the multi-object recognition and localization task as an optimization problem over the space of hypothesized scenes, ii) exploiting structure in the optimization to cast it as a combinatorial search problem on what we call the Monotone Scene Generation Tree, and iii) leveraging parallelization and recent advances in multi-heuristic search in making combinatorial search tractable. We prove that our system can guaranteedly produce the best explanation of the scene under the chosen cost function, and validate our claims on real world RGB-D test data. Our experimental results show that we can identify and localize objects under heavy occlusion- cases where state-of-the-art methods struggle.