Did you ever look for a framework that can easily compare different visual-inertial estimators in a fair and statistically relevant fashion for your robotic application?
We are excited to announce VINSEval – An open source unified evaluation framework that allows, in a fully automated fashion, a reproducible analysis on consistency and robustness of different visual-inertial estimation methods with respect to a variety of specific environmental and sensor parameters, and parameter ranges.
Some of the highlights are:
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Automated summary of the results in easy-to-read radar-charts for quick information-access with minimal user effort is generated for you
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Different photo-realistic scenes, different parameter sweeps, and different evaluation parameters can easily be included in the fully automated process
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Similarly, intrinsic sensor characteristics, camera types, rendering and extrinsic parameters, and use of real or synthetic data can easily be changed
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Time delay and synchronized or asynchronous data generation can be selected
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Output data alignment with ground truth is done for you automatically for every estimator in the comparison set
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The robustness metric is based on averaged RMSE, the consistency metric on averaged NEES
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Faster-than-real-time simulation allows for massive data generation for statistical relevant results
For the full set of features check out our ICRA21 paper, video, and code:
- Paper: https://www.aau.at/wp-content/uploads/2021/03/VINSEval_preprint.pdf
- Video: VinsEval: Unified Evaluation Framework for Visual-Inertial Navigation System Algorithms - YouTube
- Code: GitHub - aau-cns/vins_eval: VINS Eval ROS1 Workspace
Best regards
Alessandro Fornasier, Martin Scheiber, Alexander Hardt-Stremayr, Roland Jung, and Stephan Weiss
Univ.-Prof. DI Dr. Stephan Weiss
Head of Institute of Smart System Technologies
Control of Networked Systems Group
University of Klagenfurt
Universitätsstraße 65-67
9020 Klagenfurt
Austria
YouTube Channel: www.youtube.com/channel/UCAlDLtj_iHDOSCFz2fzQxWw