Robust sensor cloud localization from range measurements

G. Dubbelman, E. Duisterwinkel, L. Demi, E. Talnishnikh, H.J. Wörtche, J.W. Bergmanns

Research output: Chapter in Book/Report/Conference proceedingContribution to conference proceedingAcademicpeer-review


This work provides a feasibility study on estimating the 3-D locations of several thousand miniaturized free-floating sensor platforms. The localization is performed on basis of sparse ultrasound range measurements between sensor platforms and without the use of beacons. We show that this task can be viewed as a specific type of pose graph optimization. The main challenge is robustly estimating an initial pose graph, that models the locations of sensor platforms. For this, we introduce a novel graph growing strategy that uses random sample consensus in alternation with non-linear refinement. The theoretical properties of our sensor cloud localization method are analyzed and its robustness is investigated using simulations. These simulations are based on inlier-outlier measurement models and focus on the application of subterranean 3-D mapping of liquid environments, such as pipe infrastructures and oil wells.
Original languageEnglish
Title of host publicationProc. of the IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS)
Publication statusPublished - 2014


  • robot sensing systems
  • ultrasonic imaging


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