An Empirical Evaluation of Four Off-the-Shelf Proprietary Visual-Inertial Odometry Systems

Jungha Kim, Minkyeong Song, Yeoeun Lee, Moonkyeong Jung, Pyojin Kim
7/14/2022
cs.ROcs.CV

No confident code implementation found for this paper.

Abstract

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.

View on arXivView PDF

Code Implementations

No confident code match yet

We couldn't find an author-owned or strongly-evidenced community implementation for this paper. Any repos shown below are weak matches — verify before relying on them.

No code implementations found yet.

Know of an implementation? Let us know in the comments below!

Similar Papers

SR-LIVO: LiDAR-Inertial-Visual Odometry and Mapping with Sweep Reconstruction

85% match1 repo3852023

VINS-Mono: A Robust and Versatile Monocular Visual-Inertial State Estimator

84% match2 repos6.1k2017

SemanticSLAM: Learning based Semantic Map Construction and Robust Camera Localization

83% match1 repo162024

Brain-Inspired Visual Odometry: Balancing Speed and Interpretability through a System of Systems Approach

83% match1 repo32023

ORB-SLAM3: An Accurate Open-Source Library for Visual, Visual-Inertial and Multi-Map SLAM

83% match1 repo9.2k2020

FAST-LIO2: Fast Direct LiDAR-inertial Odometry

82% match2 repos5.3k2021

Cite this paper

@article{kim2022empirical,
  title  = {An Empirical Evaluation of Four Off-the-Shelf Proprietary Visual-Inertial Odometry Systems},
  author = {Jungha Kim and Minkyeong Song and Yeoeun Lee and Moonkyeong Jung and Pyojin Kim},
  year   = {2022},
  eprint = {2207.06780},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2207.06780v1}
}

Add a badge to your README

Shows whether official code is linked for this paper.

Code of Paper badge
[![Code of Paper](https://codeofpaper.com/badge/2207.06780.svg)](https://codeofpaper.com/paper/2207.06780)

Discussion