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20172023
most citedVisual-Inertial Navigation: A Concise Review

18 citations · 69 across the 12 of their papers we have counts for

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16 papers · 1 filter

cs.RO2023

Multi-Visual-Inertial System: Analysis, Calibration and Estimation

Yulin Yang, Patrick Geneva, Guoquan Huang

In this paper, we study state estimation of multi-visual-inertial systems (MVIS) and develop sensor fusion algorithms to optimally fuse an arbitrary number of asynchronous inertial…

cs.RO202217 cited

General Place Recognition Survey: Towards the Real-world Autonomy Age

Peng Yin, Shiqi Zhao, Ivan Cisneros +6

Place recognition is the fundamental module that can assist Simultaneous Localization and Mapping (SLAM) in loop-closure detection and re-localization for long-term navigation. The…

cs.RO2022

Observability-Aware Intrinsic and Extrinsic Calibration of LiDAR-IMU Systems

Jiajun Lv, Xingxing Zuo, Kewei Hu +3

Accurate and reliable sensor calibration is essential to fuse LiDAR and inertial measurements, which are usually available in robotic applications. In this paper, we propose a nove…

cs.RO20226 cited

Online Self-Calibration for Visual-Inertial Navigation Systems: Models, Analysis and Degeneracy

Yulin Yang, Patrick Geneva, Xingxing Zuo +1

In this paper, we study in-depth the problem of online self-calibration for robust and accurate visual-inertial state estimation. In particular, we first perform a complete observa…

cs.RO2021

Distributed Visual-Inertial Cooperative Localization

Pengxiang Zhu, Patrick Geneva, Wei Ren +1

In this paper we present a consistent and distributed state estimator for multi-robot cooperative localization (CL) which efficiently fuses environmental features and loop-closure…

cs.RO20205 cited

LIC-Fusion 2.0: LiDAR-Inertial-Camera Odometry with Sliding-Window Plane-Feature Tracking

Xingxing Zuo, Yulin Yang, Patrick Geneva +4

Multi-sensor fusion of multi-modal measurements from commodity inertial, visual and LiDAR sensors to provide robust and accurate 6DOF pose estimation holds great potential in robot…