Autonomous reconstruction of unknown indoor scenes guided by time-varying tensor fields

Kai Xu, Lintao Zheng, Zihao Yan, Guohang Yan, Eugene Zhang, Matthias Niessner, Oliver Deussen, Daniel Cohen-Or, Hui Huang*

*Corresponding author for this work

Research output: Contribution to journalConference articlepeer-review

Abstract

Autonomous reconstruction of unknown scenes by a mobile robot inherently poses the question of balancing between exploration efficacy and reconstruction quality. We present a navigation-by-reconstruction approach to address this question, where moving paths of the robot are planned to account for both global efficiency for fast exploration and local smoothness to obtain high-quality scans. An RGB-D camera, attached to the robot arm, is dictated by the desired reconstruction quality as well as the movement of the robot itself. Our key idea is to harness a time-varying tensor field to guide robot movement, and then solve for 3D camera control under the constraint of the 2D robot moving path. The tensor field is updated in real time, conforming to the progressively reconstructed scene. We show that tensor fields are well suited for guiding autonomous scanning for two reasons: first, they contain sparse and controllable singularities that allow generating a locally smooth robot path, and second, their topological structure can be used for globally efficient path routing within a partially reconstructed scene. We have conducted numerous tests with a mobile robot, and demonstrate that our method leads to a smooth exploration and high-quality reconstruction of unknown indoor scenes.

Original languageEnglish
Article numbera202
JournalACM Transactions on Graphics
Volume36
Issue number6
DOIs
StatePublished - 20 Nov 2017
Externally publishedYes
EventACM SIGGRAPH Asia Conference, SA 2017 - Bangkok, Thailand
Duration: 27 Nov 201730 Nov 2017

Keywords

  • Autonomous reconstruction
  • Camera trajectory optimization
  • Indoor scene reconstruction
  • Path planning
  • Tensor fields

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