Visual-Inertial Odometry with Online Calibration of Velocity-Control Based Kinematic Motion Models
2022
Article
ev
Visual-inertial odometry (VIO) is an important technology for autonomous robots with power and payload constraints. In this paper, we propose a novel approach for VIO with stereo cameras which integrates and calibrates the velocity-control based kinematic motion model of wheeled mobile robots online. Including such a motion model can help to improve the accuracy of VIO. Compared to several previous approaches proposed to integrate wheel odometer measurements for this purpose, our method does not require wheel encoders and can be applied when the robot motion can be modeled with velocity-control based kinematic motion model. We use radial basis function (RBF) kernels to compensate for the time delay and deviations between control commands and actual robot motion. The motion model is calibrated online by the VIO system and can be used as a forward model for motion control and planning. We evaluate our approach with data obtained in variously sized indoor environments, demonstrate improvements over a pure VIO method, and evaluate the prediction accuracy of the online calibrated model.
| Author(s): | Haolong Li and Joerg Stueckler |
| Journal: | IEEE Robotics and Automation Letters |
| Volume: | 7 |
| Number (issue): | 3 |
| Pages: | 6415--6422 |
| Year: | 2022 |
| Month: | July |
| Department(s): | Embodied Vision |
| Research Project(s): |
Visual Odometry and Simultaneous Localization and Mapping
Learning Action-Conditional Forward Models for Robot Navigation |
| Bibtex Type: | Article (article) |
| Paper Type: | Journal |
| DOI: | 10.1109/LRA.2022.3169837 |
| Note: | Accepted for oral presentation at IEEE ICRA 2023 |
| State: | Published |
| URL: | https://ieeexplore.ieee.org/document/9762544 |
| Links: |
preprint
|
|
BibTex @article{li2022_kinvio,
title = {Visual-Inertial Odometry with Online Calibration of Velocity-Control Based Kinematic Motion Models},
author = {Li, Haolong and Stueckler, Joerg},
journal = {IEEE Robotics and Automation Letters},
volume = {7},
number = {3},
pages = {6415--6422},
month = jul,
year = {2022},
note = {Accepted for oral presentation at IEEE ICRA 2023},
doi = {10.1109/LRA.2022.3169837},
url = {https://ieeexplore.ieee.org/document/9762544},
month_numeric = {7}
}
|
|
