An Empirical Evaluation of Four Off-the-Shelf Proprietary Visual-Inertial Odometry Systems
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Abstract (arXiv)
Commercial visual-inertial odometry (VIO) systems have been gaining attention as cost-effective, off-the-shelf six degrees of freedom (6-DoF) ego-motion tracking methods for estimating accurate and consistent camera pose data, in addition to their ability to operate without external localization from motion capture or global positioning systems. It is unclear from existing results, however, which commercial VIO platforms are the most stable, consistent, and accurate in terms of state estimation for indoor and outdoor robotic applications. We assess four popular proprietary VIO systems (Apple ARKit, Google ARCore, Intel RealSense T265, and Stereolabs ZED 2) through a series of both indoor and outdoor experiments where we show their positioning stability, consistency, and accuracy. We present our complete results as a benchmark comparison for the research community.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 8 of its 26 references that are in the map. First posted on arXiv 14 Jul 2022.
Builds on
8 of 26 references.
References · 8
- 2020ORB-SLAM3: An Accurate Open-Source Library for Visual, Visual-Inertial and Multi-Map SLAMcited by 862
- 2016ORB-SLAM2: an Open-Source SLAM System for Monocular, Stereo and RGB-D Camerascited by 801
- 2016Direct Sparse Odometrycited by 479
- 2017VINS-Mono: A Robust and Versatile Monocular Visual-Inertial State Estimatorcited by 418
- 2018ADVIO: An authentic dataset for visual-inertial odometrycited by 22
- 2019Learning Visuomotor Policies for Aerial Navigation Using Cross-Modal Representationscited by 18
- 2019Real-Time Dense Stereo Embedded in A UAV for Road Inspectioncited by 7
- 2018Modeling Varying Camera-IMU Time Offset in Optimization-Based Visual-Inertial Odometrycited by 2
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Citing papers per year: 2026: 1.
1 paper.