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About this paper
Dinar Sharafutdinov, Mark Griguletskii, Pavel Kopanev, Mikhail Kurenkov, Gonzalo Ferrer, Aleksey Burkov, Aleksei Gonnochenko, Dzmitry Tsetserukou
Researchers from Skoltech and Sberbank Robotics Lab performed a comprehensive comparative analysis of state-of-the-art open-source Visual SLAM and Visual-Inertial Odometry algorithms, finding ORB-SLAM3 to be consistently among the most accurate across various datasets. The study highlights practical considerations for deployment and addresses issues related to reproducibility in the field.
Key insights
- ORB-SLAM2 and ORB-SLAM3 consistently demonstrated superior localization accuracy and reliability across various indoor and outdoor datasets.
- Visual-Inertial Odometry (VIO) methods like VINS-Mono and Basalt showed competitive performance, highlighting the benefits of IMU fusion for robustness and scale estimation.
- Reproducibility remains a significant challenge in SLAM research due to varying dependencies, inconsistent documentation, and lack of consistent developer support for open-source implementations.
Summary by alphaXiv.
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