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Accurate Registration of Mms Point Clouds of Urban Areas Using Trajectory : Volume Ii-5/W2, Issue 1 (16/10/2013)

By Takai, S.

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Book Id: WPLBN0004014132
Format Type: PDF Article :
File Size: Pages 6
Reproduction Date: 2015

Title: Accurate Registration of Mms Point Clouds of Urban Areas Using Trajectory : Volume Ii-5/W2, Issue 1 (16/10/2013)  
Author: Takai, S.
Volume: Vol. II-5/W2, Issue 1
Language: English
Subject: Science, Isprs, Annals
Collections: Periodicals: Journal and Magazine Collection, Copernicus GmbH
Historic
Publication Date:
2013
Publisher: Copernicus Gmbh, Göttingen, Germany
Member Page: Copernicus Publications

Citation

APA MLA Chicago

Ikeda, T., Kanai, S., Niina, Y., Date, H., Takai, S., & Oda, K. (2013). Accurate Registration of Mms Point Clouds of Urban Areas Using Trajectory : Volume Ii-5/W2, Issue 1 (16/10/2013). Retrieved from http://www.hawaiilibrary.net/


Description
Description: Graduate School of Information Science and Technology, Hokkaido University, Sapporo 060-0814, Japan. Recently, by Mobile Mapping System (MMS) with laser scanners, a GPS and IMU (Inertial Measurement Unit), 3D point clouds of urban areas (MMS point clouds) are easily acquired. When the same areas are scanned several times by the MMS, the point clouds often have differences in the range of several hundreds of millimetres. Such differences are caused by inertial drifts of IMU and losses of GPS signals in urban areas. In this paper, we propose an automatic accurate registration method of MMS point clouds using a new variant of ICP (Iterative Closest Point) algorithm for MMS point clouds and trajectory modification. Our method consists of four steps. Firstly, some trajectory points are automatically extracted by analyzing the trajectory. Secondly, the differences of point clouds are derived at the extracted trajectory points in the overlapping scan region by our new ICP algorithm which minimizes pointto- plane and point-to-point distances simultaneously and filters incorrect correspondences based on a point classification by PCA (Principle Component Analysis). Thirdly, the modified positions and rotation parameters at all extracted trajectory points are derived by a least squares method for positioning and registration constraints. Finally, each point in the point clouds is modified by coordinate transformations which are derived from linear interpolation of the modified positions and rotation parameters of the extracted trajectory points. Our method was applied to MMS point clouds and trajectory and the performances were evaluated.

Summary
Accurate registration of MMS point clouds of urban areas using trajectory

 

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