Efficient next-best-scan planning for autonomous 3D surface reconstruction of unknown objects


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.

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This work has partly been supported by the European Commission under contract number FP7-ICT-260026-TAPAS. The authors would like to thank the editor and all the reviewers for their constructive comments. Our special thanks go to Daniel Seth for his support with the octree structure, Andreas Dömel for his help with the path planner, Klaus Strobl for helping with the sensor calibration and Zoltan-Csaba Marton for good ideas and feedback.

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Correspondence to Simon Kriegel.

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Kriegel, S., Rink, C., Bodenmüller, T. et al. Efficient next-best-scan planning for autonomous 3D surface reconstruction of unknown objects. J Real-Time Image Proc 10, 611–631 (2015). https://doi.org/10.1007/s11554-013-0386-6

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  • 3D modeling
  • Next-best-view planning
  • Active vision
  • Laser scanning