Abstract
Kinect style depth cameras provide RGB images along with pre-pixel depth information, the richness of their data and recent development of low-cost sensors have made them more popular in mobile robotics research. In this paper, we present a framework of dense 3D mapping. Sparse visual features are used to determine an initial rough transformation, then it is refined by color-GICP (General iterative closest point). We employ a window sparse bundle adjustment to optimize the local map after it is constructed and a new keyframe is created at the same time. Visual features and dense information are also used in loop closure detection, following by a globally consistent optimization based on graph. Moreover, we introduce a user interaction to improve the map building progress. This proposed approach is evaluated by the RGB-D benchmark and two real indoor environments, and experiment results show the feasibility and effectiveness of this approach.
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Wang, Y., Zhang, Q., Zhou, Y. (2015). Dense 3D Mapping for Indoor Environment Based on Kinect-Style Depth Cameras. In: Kim, JH., Yang, W., Jo, J., Sincak, P., Myung, H. (eds) Robot Intelligence Technology and Applications 3. Advances in Intelligent Systems and Computing, vol 345. Springer, Cham. https://doi.org/10.1007/978-3-319-16841-8_30
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DOI: https://doi.org/10.1007/978-3-319-16841-8_30
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-16840-1
Online ISBN: 978-3-319-16841-8
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