FEJ-VIRO: A Consistent First-Estimate Jacobian Visual-Inertial-Ranging Odometry

Jia, Shenhan; Jiao, Yanmei; Zhang, Zhuqing; Xiong, Rong; Wang, Yue

doi:10.1109/iros47612.2022.9981413

Cited by 13 publications

(6 citation statements)

References 25 publications

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“…VIR-SLAM [6] and Omni-swarm [34] estimate the global pose by the VIO and UWB for multi-robot. FEJ-VIRO [13] fuses the VIO and UWB under the MSCKF frame. VINS-Fusion [24] proposes a general framework to fuse various global sensors with local estimations.…”

Section: Multi-sensor Fusion For Unmanned Aerial Vehiclesmentioning

confidence: 99%

GRVINS: Tightly Coupled GNSS-Range-Visual-Inertial System

Lu,

Tsai,

Tseng

2024

J Intell Robot Syst

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Bridge inspection is currently a labor intensive task. Utilizing unmanned aerial vehicles (UAVs) to assist in inspection tasks is a promising direction. However, enabling UAVs for autonomous inspection involves the UAV state estimation problems. Since parts of UAV sensors could be unavailable, how to estimate states via sensor fusion is the key. In this paper, we propose a tightly-coupled nonlinear optimization-based system that integrates four kinds of sensors: camera, IMU, Ultra-wideband (UWB) range measurements, and global navigation satellite system (GNSS). Due to the tightly-coupled multi-sensor fusion method and system design, the system takes the advantage of the four sensors, and can seamlessly respond to indoor and outdoor GNSS and UWB loss or reacquisition. It can effectively reduce the long-term trajectory drift and provide smooth and continuous state estimation. The experimental results show that the proposed method outperforms the state-of-the-art approaches.

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Section: Multi-sensor Fusion For Unmanned Aerial Vehiclesmentioning

confidence: 99%

GRVINS: Tightly Coupled GNSS-Range-Visual-Inertial System

Lu,

Tsai,

Tseng

2024

J Intell Robot Syst

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“…, where 18,18), ([15, 16, 17, 4], [15,16,17,5], [1, 1, 1, −1], 18, 18), [6,9,12,18], [1, 1, 1, −1], 18, 18), [7,10,13,18], [1, 1, 1, −1], 18, 18), [8,11,14,18], [1, 1, 1, −1], 18, 18), P 6 = sparse([4], [18], [1], 18, 18), (42) P 7 = sparse( [18], [18], [1], 18,18),…”

Section: Proposed Systemmentioning

confidence: 99%

“…where P 8 = sparse([6, 9, 12, 5], [6,9,12,18], [1, 1, 1, −1], 18, 18), P 9 = sparse([7, 10, 13, 5], [7,10,13,18], [1, 1, 1, −1], 18, 18), P 10 = sparse( [8,11,14,5], [8,11,14,18], [1, 1, 1, −1], 18, 18), P 11 = sparse([6, 9, 12], [7,10,13], [1, 1, 1], 18, 18), P 12 = sparse( [6,9,12], [8,11,14], [1, 1, 1], 18, 18), (43) P 13 = sparse( [7,10,13], [8,11,14], [1, 1, 1], 18, 18), p 8 = 0, p 9 = 0, p 10 = 0, p 11 = 0, p 12 = 0, p 13 = 0.…”

Section: Proposed Systemmentioning

confidence: 99%

“…We report the results for the real-time (RT) position estimates and the final optimized trajectory (OT), i.e., all KF poses after they are corrected by visual loop closure and UPGO modules. We also include the RT results of VINS-Fusion [33], a visual-inertial odometry method, and VIRO-FEJ [14], a visual-inertial-range odometry III, it can be seen that our OT results are the most accurate solution, while UWB-only localization is the least. The UWBonly localization results are stable across the dataset, which can be an advantage in terms of reliability.…”

Section: B Public Datasetmentioning

confidence: 99%

“…As such, using UWB to complement other sensor modalities has great potential for real-life applications. Many researches have been put forth to combine UWB with RGB-D camera [10], camera and GPS [11], IMU [12], IMU and camera [13], [14] or LiDAR [15] and a combination of them [16]. Among these options, UWB and camera are not only low-cost and low-power but also mechanically flexible to install on robotic platforms.…”

Section: Introductionmentioning

confidence: 99%

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VR-SLAM: A Visual-Range Simultaneous Localization and Mapping System using Monocular Camera and Ultra-wideband Sensors

Nguyen¹,

Yuan²,

Xie³

2023

Preprint

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In this work, we propose a simultaneous localization and mapping (SLAM) system using a monocular camera and Ultra-wideband (UWB) sensors. Our system, referred to as VR-SLAM, is a multi-stage framework that leverages the strengths and compensates for the weaknesses of each sensor. Firstly, we introduce a UWB-aided 7 degree-of-freedom (scale factor, 3D position, and 3D orientation) global alignment module to initialize the visual odometry (VO) system in the world frame defined by the UWB anchors. This module loosely fuses up-to-scale VO and ranging data using either a quadratically constrained quadratic programming (QCQP) or nonlinear least squares (NLS) algorithm based on whether a good initial guess is available. Secondly, we provide an accompanied theoretical analysis that includes the derivation and interpretation of the Fisher Information Matrix (FIM) and its determinant. Thirdly, we present UWBaided bundle adjustment (UBA) and UWB-aided pose graph optimization (UPGO) modules to improve short-term odometry accuracy, reduce long-term drift as well as correct any alignment and scale errors. Extensive simulations and experiments show that our solution outperforms UWB/camera-only and previous approaches, can quickly recover from tracking failure without relying on visual relocalization, and can effortlessly obtain a global map even if there are no loop closures.

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