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  4. A Tutorial on Quantitative Trajectory Evaluation for Visual(-Inertial) Odometry
 
conference paper

A Tutorial on Quantitative Trajectory Evaluation for Visual(-Inertial) Odometry

Zhang, Zichao
•
Scaramuzza, Davide
January 7, 2019
2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

In this tutorial, we provide principled methods to quantitatively evaluate the quality of an estimated trajectory from visual(-inertial) odometry (VO/VIO), which is the foundation of benchmarking the accuracy of different algorithms. First, we show how to determine the transformation type to use in trajectory alignment based on the specific sensing modality (i.e., monocular, stereo and visual-inertial). Second, we describe commonly used error metrics (i.e., the absolute trajectory error and the relative error) and their strengths and weaknesses. To make the methodology presented for VO/VIO applicable to other setups, we also generalize our formulation to any given sensing modality. To facilitate the reproducibility of related research, we publicly release our implementation of the methods described in this tutorial.

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Type
conference paper
DOI
10.1109/IROS.2018.8593941
Author(s)
Zhang, Zichao
Scaramuzza, Davide
Date Issued

2019-01-07

Published in
2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
Start page

7244

End page

7251

Subjects

Trajectory

•

Cameras

•

Noise measurement

•

Tutorials

•

Sensors

•

Visualization

Note

Open Source Code: A trajectory evaluation toolbox that implements the methods in this tutorial is available at https://github.com/uzh-rpg/rpg_trajectory_evaluation.

Editorial or Peer reviewed

REVIEWED

Written at

OTHER

EPFL units
NCCR-ROBOTICS  
Event nameEvent placeEvent date
2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

Madrid, Spain

1-5 October 2018

Available on Infoscience
October 31, 2019
Use this identifier to reference this record
https://infoscience.epfl.ch/handle/20.500.14299/162550
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