Tightly coupled Visual Inertial Odometry based on Artificial Landmarks

Song, Junlin; Zheming, Liu; Liu, Xin; Guo, Jifeng

doi:10.1109/icinfa.2018.8812447

Cited by 4 publications

(3 citation statements)

References 15 publications

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“…The VIO can be further classified based on type of data fusion into filtering-based [153], [159], [162], [167], [181] and optimization-based [145], [158], [190]- [193] solutions. In general, performing state estimation using Filtering-based VIO is processed in two stages, (i) estimate the vehicle pose using the IMU linear acceleration and angular velocities that drive the vehicle dynamic model and (ii) update the vehicle pose using the key information of the visual data that estimated the vehicle ego-motion.…”

Section: Discussion and Future Research Directionsmentioning

confidence: 99%

State of the Art in Vision-Based Localization Techniques for Autonomous Navigation Systems

2021

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Vision-based localization systems, namely visual odometry (VO) and visual inertial odometry (VIO), have attracted great attention recently. They are regarded as critical modules for building fully autonomous systems. The simplicity of visual and inertial state estimators, along with their applicability in resource-constrained platforms motivated robotic community to research and develop novel approaches that maximize their robustness and reliability. In this paper, we surveyed state-of-the-art VO and VIO approaches. In addition, studies related to localization in visually degraded environments are also reviewed. The reviewed VO techniques and related studies have been analyzed in terms of key design aspects including appearance, feature, and learning based approaches. On the other hand, research studies related to VIO have been categorized based on the degree and type of fusion process into loosely-coupled, semi-tightly coupled, or tightly-coupled approaches and filtering or optimization-based paradigms. This paper provides an overview of the main components of visual localization, key design aspects highlighting the pros and cons of each approach, and compares the latest research works in this field. Finally, a detailed discussion of the challenges associated with the reviewed approaches and future research considerations are formulated.

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Section: Discussion and Future Research Directionsmentioning

confidence: 99%

State of the Art in Vision-Based Localization Techniques for Autonomous Navigation Systems

2021

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“…The method was tested onboard a UAV with promising results but limited to indoor environments. Conceptually, Song et al [35] propose a similar solution. However, they simplify the design of the inner codification of the tags to speed up the detection frequency.…”

Section: A Related Workmentioning

confidence: 99%

Development and Experimental Validation of Visual-Inertial Navigation for Auto-Docking of Unmanned Surface Vehicles

2023

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Docking is a safety-critical operation for autonomous surface vehicles and requires highly accurate navigation signals. Since Global Navigation Satellite Systems (GNSS) can be unreliable and inaccurate in urban environments, other sensors should be considered for increased redundancy and reliability. To this end, we present a low-cost visual-inertial navigation system that we can use for automatic docking of small vehicles. The proposed system produces state estimates of the vehicle, including position, velocity, and attitude, based on raw image and inertial data. To simplify the navigation task, we use easily identifiable tags as a reference on the dockside. When the vehicle approaches the dock, a visual fiducial system recognizes the tags and estimates the relative pose between the camera onboard the vehicle and the tags at the dockside. The camera-tag pose and inertial data are then fused using an error-state Kalman filter for robust state estimation of the vehicle. For benchmarking, we use an unmanned surface vehicle equipped with a dual-antenna realtime kinematic GNSS receiver for accurate positioning and heading. We show that the proposed method performs well on regular and adverse weather data. Finally, we demonstrate that the proposed method performs well in feedback control through field experiments and can supplement traditional navigation systems for docking operations.INDEX TERMS Autonomous docking, fiducial tags, unmanned surface vehicles, visual-inertial state estimation.

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“…Some visual methods to calculate pose of the camera have also been developed, [10][11][12][13] which is known as camera pose estimation, and many fields are used but not robotics monitor. Here, though meeting with some problems that IMU would not come up with, visual methods have some advantage that IMUs do not have.…”

Section: Introductionmentioning

confidence: 99%

Industry robotic motion and pose recognition method based on camera pose estimation and neural network

Wang

Xie

Yang

et al. 2021

International Journal of Advanced Robotic Systems

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To control industry robots and make sure they are working in a correct status, an efficient way to judge the motion of the robot is important. In this article, an industry robotic motion and pose recognition method based on camera pose estimation and neural network are proposed. Firstly, industry robotic motion recognition based on the neural network has been developed to estimate and optimize motion of the robotics only by a monoscope camera. Secondly, the motion recognition including key flames recording and pose adjustment has been proposed and analyzed to restore the pose of the robotics more accurately. Finally, a KUKA industry robot has been used to test the proposed method, and the test results have demonstrated that the motion and pose recognition method can recognize the industry robotic pose accurately and efficiently without inertial measurement unit (IMU) and other censers. Below in the same algorithm, the error of the method introduced in this article is better than the traditional method using IMU and has a better merit of reducing cumulative error.

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Tightly coupled Visual Inertial Odometry based on Artificial Landmarks

Cited by 4 publications

References 15 publications

State of the Art in Vision-Based Localization Techniques for Autonomous Navigation Systems

State of the Art in Vision-Based Localization Techniques for Autonomous Navigation Systems

Development and Experimental Validation of Visual-Inertial Navigation for Auto-Docking of Unmanned Surface Vehicles

Industry robotic motion and pose recognition method based on camera pose estimation and neural network

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