TY - GEN
T1 - Lidar scan matching with rtk-gnss positioning and geometric constraints
AU - Nakagawa, Masafumi
AU - Abe, Shinjiro
AU - Sanuka, Sho
AU - Saito, Kazuo
AU - Miyo, Masahiro
N1 - Publisher Copyright:
© 2020 ACRS 2020 - 41st Asian Conference on Remote Sensing. All rights reserved.
PY - 2020
Y1 - 2020
N2 - Scan matching in simultaneous localization and mapping (SLAM) has several technical issues, such as error accumulation, high processing cost in point cloud matching and optimization, and position loss problems after scan matching failures. Error adjustment processing can improve the performance of SLAM with loop closure and global optimization approach. However, measurement path plans and higher processing cost are required for the error adjustment and global optimization. In contrast, global navigation satellite system (GNSS) positioning can simplify the scan matching. Thus, we propose scan matching for multilayer LiDAR data registration with RTK-GNSS positioning and geometric constraints. Through experiments on point cloud acquisition with multilayer LiDAR and a single-frequency RTK-GNSS positioning device, we verify that our methodology can integrate point clouds acquired in mobile mapping without an inertial measurement unit. We also confirm that our methodology can avoid error accumulation problems in conventional SLAM processing.
AB - Scan matching in simultaneous localization and mapping (SLAM) has several technical issues, such as error accumulation, high processing cost in point cloud matching and optimization, and position loss problems after scan matching failures. Error adjustment processing can improve the performance of SLAM with loop closure and global optimization approach. However, measurement path plans and higher processing cost are required for the error adjustment and global optimization. In contrast, global navigation satellite system (GNSS) positioning can simplify the scan matching. Thus, we propose scan matching for multilayer LiDAR data registration with RTK-GNSS positioning and geometric constraints. Through experiments on point cloud acquisition with multilayer LiDAR and a single-frequency RTK-GNSS positioning device, we verify that our methodology can integrate point clouds acquired in mobile mapping without an inertial measurement unit. We also confirm that our methodology can avoid error accumulation problems in conventional SLAM processing.
KW - Geometric constraints
KW - Mobile mapping
KW - Multilayer LiDAR
KW - RTK-GNSS
KW - Scan matching
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M3 - Conference contribution
AN - SCOPUS:85107229843
T3 - ACRS 2020 - 41st Asian Conference on Remote Sensing
BT - ACRS 2020 - 41st Asian Conference on Remote Sensing
PB - Asian Association on Remote Sensing
T2 - 41st Asian Conference on Remote Sensing, ACRS 2020
Y2 - 9 November 2020 through 11 November 2020
ER -