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Home > Archive>Volume 27, Issue 10, 2016 >2661-2675. DOI:10.13328/j.cnki.jos.005083
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Optical Image Based Multi-Granularity Follow-Up Environment Perception Algorithm
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National Natural Science Foundation of China (61170223, 61202207, 61502432)

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    Abstract:

    An optical image based multi-granularity follow-up environment perception algorithm is proposed to address the follow-up environment perception issue from indoor to outdoor in the field of rapid 3D modeling. The algorithm generates multi-granularity 3D point cloud models which perfectly fit the ground-truth according to different types of optical image. A probabilistic octree representation is proposed to uniformly express the 3D point cloud models. Finally, the expected TFPOM is generated through dynamic ground-truth fitting at any granularity, and probabilistic octree representation of multi-granularity point cloud models are dynamically fused through implementation of Kalman filter along with the camera trajectory. Benefiting from pruning and merging strategies, the proposed algorithm meets requirements of multi-granularity fusion and multi-granularity representation. As a result, the storage space of environment models can be effectively compressed and robust follow-up environment perception can be achieved, which are essential in environment model based visual navigation and augmented reality. Experiment results show that the algorithm can generate multi-granularity TFPOM which perfectly fits ground-truth in real time with fewer errors in model based navigation on platforms, such as wearable devices, that are equipped with multiple optical sensors and low computing capability.

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陈昊升,张格,叶阳东.基于光学图像的多粒度随动环境感知算法.软件学报,2016,27(10):2661-2675

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History
  • Received:January 20,2016
  • Revised:March 25,2016
  • Online: August 11,2016
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