Version 1
: Received: 10 January 2024 / Approved: 11 January 2024 / Online: 11 January 2024 (10:19:02 CET)
How to cite:
Benevides, R.A.L.; Dos Santos, D.R.; Pavan, N.L. Advancing Global Pose Refinement: A Linear, Parameter-Free Model for Closed Circuits via Quaternion Interpolation. Preprints2024, 2024010903. https://doi.org/10.20944/preprints202401.0903.v1
Benevides, R.A.L.; Dos Santos, D.R.; Pavan, N.L. Advancing Global Pose Refinement: A Linear, Parameter-Free Model for Closed Circuits via Quaternion Interpolation. Preprints 2024, 2024010903. https://doi.org/10.20944/preprints202401.0903.v1
Benevides, R.A.L.; Dos Santos, D.R.; Pavan, N.L. Advancing Global Pose Refinement: A Linear, Parameter-Free Model for Closed Circuits via Quaternion Interpolation. Preprints2024, 2024010903. https://doi.org/10.20944/preprints202401.0903.v1
APA Style
Benevides, R.A.L., Dos Santos, D.R., & Pavan, N.L. (2024). Advancing Global Pose Refinement: A Linear, Parameter-Free Model for Closed Circuits via Quaternion Interpolation. Preprints. https://doi.org/10.20944/preprints202401.0903.v1
Chicago/Turabian Style
Benevides, R.A.L., Daniel Rodrigues Dos Santos and Nadisson Luis Pavan. 2024 "Advancing Global Pose Refinement: A Linear, Parameter-Free Model for Closed Circuits via Quaternion Interpolation" Preprints. https://doi.org/10.20944/preprints202401.0903.v1
Abstract
Global pose refinement is a significant challenge within Simultaneous Localization and Mapping (SLAM) frameworks. For LIDAR-based SLAM systems, pose refinement is integral to correcting drift caused by the successive registration of 3D point clouds collected by the sensor. A divergence between the actual and calculated platform path characterizes this error. In response to this challenge, we propose a linear, parameter-free model, which uses a closed circuit for global trajectory corrections. Our model maps rotations to quaternions and uses Spherical Linear Interpolation (SLERP) for transitions between them. The intervals are established from the constraint set by the Least Squares (LS) method on rotation closure and are proportional to the circuit's size. Translations are globally adjusted in a distinct linear phase. Additionally, we suggest a coarse-to-fine pairwise registration method, integrating Fast Global Registration and Generalized ICP with multiscale sampling and filtering. The proposed approach is tested on three varied datasets of point clouds, including Mobile Laser Scanners and Terrestrial Laser Scanners. These diverse datasets affirm the model's effectiveness in 3D pose estimation with substantial pose differences and efficient pose optimization in larger circuits.
Keywords
Point Clouds; SLERP; least squares; slam; laser scanning; global pose refinement; pairwise registration
Subject
Computer Science and Mathematics, Computer Vision and Graphics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
