Autonomous Mobile Robot

This study is a part of collaborative research aiming at Autonomous Mobile Robots (AMRs), unmanned vehicles that convey parts in factories. Specifically, this study addresses the creation of a Point Cloud Data (PCD) map for the workspace of AMRs.

For self-localization, a map containing the current position is usually required in advance. This map should be created in a format compatible with the sensors on AMRs. As our AMRs are equipped with 3D LiDAR, a Point Cloud Data (PCD) map should be prepared.

In the conventional method, the LiDAR on the AMRs is utilized. This method requires scanning the workspace by running the AMRs manually before they are actually introduced. This study establishes a method to create the PCD map from the design drawings, combining tablet PC scans for the objects not existing in the drawings.

Papers

3D PCD map creation for LiDAR localization using a spherical camera
Proceedings of the SICE Annual Conference 2021 , pp. 404-409 (2021)
Yusuke Isozumi Satoshi Ito, Ryosuke Morita, Yuki Funabora

PCD map creation from factory design drawing for LiDAR self-localization of autonomous mobile robot
Proc. of the Twenty-Seventh International Symposium on Artificial Life and Robotics 2022 (AROB 27th 2022) , pp. 569-574 (2022)
Ryutaro Kaneko, Yuji Nakamura, Ryosuke Morita and Satoshi Ito

A Map Creation for LiDAR Localization Based on the Design Drawings and Tablet Scan Data
Journal of Robotics and Mechatronics , Vol. 35 , No. 2 , pp. 470-482 (2023)
Satoshi Ito, Ryutaro Kaneko, Takumi Saito, and Yuji Nakamura

Point cloud data map creation from factory design drawing for LiDAR localization of an autonomous mobile robot
Artif Life Robotics , Vol. 28 , No. 2 , pp. 314-322 (2023)
Ryutaro Kaneko, Yuji Nakamura, Ryosuke Morita, Satoshi Ito