Efficient next-best-scan planning for autonomous 3D surface reconstruction of unknown objects
Efficient next-best-scan planning for autonomous 3D surface reconstruction of unknown objects
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DOI：
10.1007/s11554-013-0386-6
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发表时间：
2015-12-01
影响因子：
3
通讯作者：
Suppa, Michael
中科院分区：
文献类型：
--
作者：
Kriegel, Simon;Rink, Christian;Suppa, Michael
这项工作的重点是利用机器人和3D传感器对小尺寸物体进行自主表面重建。目标是一个高质量的表面模型，允许机器人应用，如抓取和操作。我们的方法包括生成次优扫描（NBS）候选项和选择标准，扫描补丁之间的错误最小化和终止标准。通过对获取的模型进行边界检测和表面趋势估计，迭代确定NBS候选点。考虑到快速和高质量的模型获取，该候选模型被选择为NBS，它最大化了集成了勘探和网格质量组件的效用函数。以具有高精度激光剥离系统的工业机器人为研究对象，对其建模和扫描规划方法进行了评价。在执行新的激光扫描时，数据被实时集成到三角形网格和概率体素空间中。通过不同的文物、家居和工业对象，验证了该系统在快速获取高质量三维表面模型方面的效率。
This work focuses on autonomous surface reconstruction of small-scale objects with a robot and a 3D sensor. The aim is a high-quality surface model allowing for robotic applications such as grasping and manipulation. Our approach comprises the generation of next-best-scan (NBS) candidates and selection criteria, error minimization between scan patches and termination criteria. NBS candidates are iteratively determined by a boundary detection and surface trend estimation of the acquired model. To account for both a fast and high-quality model acquisition, that candidate is selected as NBS, which maximizes a utility function that integrates an exploration and a mesh-quality component. The modeling and scan planning methods are evaluated on an industrial robot with a high-precision laser striper system. While performing the new laser scan, data are integrated on-the-fly into both, a triangle mesh and a probabilistic voxel space. The efficiency of the system in fast acquisition of high-quality 3D surface models is proven with different cultural heritage, household and industrial objects.