Indoor map generation from multiple LiDAR point clouds
H Yoshisada, Y Yamada, A Hiromori… - … on Smart Computing …, 2018 - ieeexplore.ieee.org
This paper presents a new algorithm for building an indoor map by integrating point clouds
of 2D light detection and ranging (LIDAR) scanners in indoor environments. Iterative closest
point (ICP) algorithm is one of the well-known methods for such purpose and often used for
mobile robot SLAM. However, the algorithm is designed based on" dense"(or" continuous")
measurement of the same space with known relative positions of measurement points and
angles, and it does not often work efficiently if the measurement is sparse and/or LIDAR …
of 2D light detection and ranging (LIDAR) scanners in indoor environments. Iterative closest
point (ICP) algorithm is one of the well-known methods for such purpose and often used for
mobile robot SLAM. However, the algorithm is designed based on" dense"(or" continuous")
measurement of the same space with known relative positions of measurement points and
angles, and it does not often work efficiently if the measurement is sparse and/or LIDAR …
Indoor Map Generation from Multiple LIDAR Point Clouds
H Yamaguchi, T Higashino, Y Yamada… - 2018 IEEE International …, 2018 - cir.nii.ac.jp
This paper presents a new algorithm for building an indoor map by integrating point clouds
of 2D light detection and ranging (LIDAR) scanners in indoor environments. Iterative closest
point (ICP) algorithm is one of the well-known methods for such purpose and often used for
mobile robot SLAM. However, the algorithm is designed based on" dense"(or" continuous")
measurement of the same space with known relative positions of measurement points and
angles, and it does not often work efficiently if the measurement is sparse and/or LIDAR …
of 2D light detection and ranging (LIDAR) scanners in indoor environments. Iterative closest
point (ICP) algorithm is one of the well-known methods for such purpose and often used for
mobile robot SLAM. However, the algorithm is designed based on" dense"(or" continuous")
measurement of the same space with known relative positions of measurement points and
angles, and it does not often work efficiently if the measurement is sparse and/or LIDAR …
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