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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 215))

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Abstract

This paper presents an implementation of indoor Simultaneous Localization and Mapping (SLAM) using RGBD images. Such system can be used in applications such as indoor robot navigation and environment perception. We perform coarse frame alignments using visual features. The coarse alignment results are then refined by applying Iterative Closest Point (ICP) algorithm to the point clouds. We create a pose graph which consists of keyframes which will be optimized if a new loop is detected. The performances of coarse alignment are tested using four methods—KLT tracker, SIFT, SURF and ORB. The experiment results show that ORB is a good trade-off between accuracy and efficiency. The performances and limitations of ICP are also explored. The results indicate that ICP is very sensitive to the initial value and the size of the point clouds. We also find that the loop closing largely reduces the alignment error. The maps of our laboratory are created using both the 3D point clouds and octomap.

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Correspondence to Rongyi Lin .

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Lin, R., Wang, Y., Yang, S. (2014). RGBD SLAM for Indoor Environment. In: Sun, F., Hu, D., Liu, H. (eds) Foundations and Practical Applications of Cognitive Systems and Information Processing. Advances in Intelligent Systems and Computing, vol 215. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37835-5_15

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  • DOI: https://doi.org/10.1007/978-3-642-37835-5_15

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-37834-8

  • Online ISBN: 978-3-642-37835-5

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