Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/145341
Title: SCALAR - simultaneous calibration of 2D laser and robot's kinematic parameters using three planar constraints
Authors: Lembono, Teguh Santoso
Suarez-Ruiz, Francisco
Pham, Quang-Cuong
Keywords: Engineering::Mechanical engineering
Issue Date: 2019
Source: Lembono, T. S., Suarez-Ruiz, F., & Pham, Q.-C. (2018). SCALAR - simultaneous calibration of 2D laser and robot's kinematic parameters using three planar constraints. Proceedings of 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 5570-5575. doi:10.1109/IROS.2018.8594073
Project: NGF-2016-01-028
NG000074-ENG
Abstract: Industrial robots are increasingly used in various applications where the robot accuracy becomes very important, hence calibrations of the robot's kinematic parameters and the measurement system's extrinsic parameters are required. However, the existing calibration approaches are either too cumbersome or require another expensive external measurement system such as laser tracker or measurement spinarm. In this paper, we propose SCALAR, a calibration method to simultaneously improve the kinematic parameters of a 6-DoF robot and the extrinsic parameters of a 2D Laser Range Finder (LRF) that is attached to the robot. Three flat planes are placed around the robot, and for each plane the robot moves to several poses such that the LRF's ray intersect the respective plane. Geometric planar constraints are then used to optimize the calibration parameters using Levenberg-Marquardt nonlinear optimization algorithm. We demonstrate through simulations that SCALAR can reduce the average position and orientation errors of the robot system from 14.6 mm and 4.05° to 0.09 mm and 0.02°.
URI: https://hdl.handle.net/10356/145341
ISBN: 978-1-5386-8095-7
DOI: 10.1109/IROS.2018.8594073
Rights: © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: https://doi.org/10.1109/IROS.2018.8594073
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:MAE Conference Papers

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