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Multi-scale Tetrahedral Fusion of a Similarity Reconstruction and Noisy Positional Measurements

  • Runze Zhang
  • Tian FangEmail author
  • Siyu Zhu
  • Long Quan
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9004)

Abstract

The fusion of a 3D reconstruction up to a similarity transformation from monocular videos and the metric positional measurements from GPS usually relies on the alignment of the two coordinate systems. When positional measurements provided by a low-cost GPS are corrupted by high-level noises, this approach becomes problematic. In this paper, we introduce a novel framework that uses similarity invariants to form a tetrahedral network of views for the fusion. Such a tetrahedral network decouples the alignment from the fusion to combat the high-level noises. Then, we update the similarity transformation each time a well-conditioned motion of cameras is successfully identified. Moreover, we develop a multi-scale sampling strategy to reduce the computational overload and to adapt the algorithm to different levels of noises. It is important to note that our optimization framework can be applied in both batch and incremental manners. Experiments on simulations and real datasets demonstrate the robustness and the efficiency of our method.

Keywords

Global Position System Unmanned Aerial Vehicle Positional Measurement Similarity Transformation Bundle Adjustment 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgement

Real-time videos Garden, House, Park are provided by DJI and CAMPUS is provided by Key Laboratory of Machine Perception (Ministry of Education) in Peking University. This work is supported by RGC-GRF 618711, RGC/NSFC N_HKUST607/11, ITC-PSKL12EG02, and National Basic Research Program of China (2012CB316300).

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.The Hong Kong University of Science and TechnologyHong KongChina

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