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Cited by 8Robotics and Sensor-Based LocalizationInertial Sensor and NavigationTarget Tracking and Data Fusion in Sensor Networks

Depth enhanced visual-inertial odometry based on Multi-State Constraint Kalman Filter

Pang, Fumin, Chen, Zichong, Pu, Li, Wang, Tianmiao·2017·2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
DOI 10.1109/iros.2017.8205989
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Abstract

There have been increasing demands for developing robotic system combining camera and inertial measurement unit in navigation task, due to their low-cost, lightweight and complementary properties. In this paper, we present a Visual Inertial Odometry (VIO) system which can utilize sparse depth to estimate 6D pose in GPS-denied and unstructured environments. The system is based on Multi-State Constraint Kalman Filter (MSCKF), which benefits from low computation load when compared to optimization-based method, especially on resource-constrained platform. Features are enhanced with depth information forming 3D landmark position measurements in space, which reduces uncertainty of position estimate. And we derivate measurement model to access compatibility with both 2D and 3D measurements. In experiments, we evaluate the performance of the system in different in-flight scenarios, both cluttered room and industry environment. The results suggest that the estimator is consistent, substantially improves the accuracy compared with original monocular-based MSKCF and achieves competitive accuracy with other research.

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Crossref · 10.1109/iros.2017.8205989

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  • MSCKF approaches are typically less accurate than optimization-based approaches.

    “While its complexity is linear in the number of features being used for egomotion estimation and it was generally more robust compared to optimization-based approaches (as has been recently reported in [22]), MSCKF approaches are typically less accurate in comparison.”

    From Unsupervised Deep Visual-Inertial Odometry with Online Error Correction for RGB-D Imagery (2020) · cites this paper as [22]

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Authors
Pang, F., Chen, Z., Pu, L., & Wang, T.
Affiliations
Beihang University (CN)
ORCID
Venue
2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
Publisher
IEEE
Published
2017-09
Pages
1761-1767
ISSN
2153-0858 (ISSN-L)
DOI
10.1109/iros.2017.8205989
OpenAlex
W2773206443
Cited by
8
References
22
Citations by year
2022: 1 · 2021: 1 · 2020: 1 · 2019: 2 · 2018: 3
FWCI
2.87
Type
conference paper
Language
en
Open access
closed
Locations
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (publishedVersion)
Topics
  • Physical Sciences › Engineering › Aerospace Engineering › Robotics and Sensor-Based Localization
  • Physical Sciences › Engineering › Aerospace Engineering › Inertial Sensor and Navigation
  • Physical Sciences › Computer Science › Artificial Intelligence › Target Tracking and Data Fusion in Sensor Networks
Keywords
visual-inertial odometry, multi-state constraint Kalman filter, depth information, 6D pose estimation, GPS-denied navigation, monocular camera, inertial measurement unit, resource-constrained platforms

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