Seamless integration of polarization compass and inertial navigation data with a self-learning multi-rate residual correction algorithm

Zhao, Deli; Liu, Xiaochen; Zhao, Huijun; Wang, Chenguang; Tang, Jun; Liu, Jun; Shen, Chong

doi:10.1016/j.measurement.2020.108694

Cited by 38 publications

(6 citation statements)

References 30 publications

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“…8,9 The major disadvantages of these methods are their high-cost computational resources and difficulty adapting to unexpected occurrences in the working environment, making these methods difficult to implement in real-time applications. In the second group, advanced algorithms such as particle swarm optimization, 10 artificial bee colony, 11 cuckoo search, 12 firefly algorithm, 13 and data fusion 14 are suggested for mobile robot navigation over conventional methods. These methods have the potential ability to deal with uncertainty when the robot moves alone.…”

Section: Introductionmentioning

confidence: 99%

Visual application of navigation framework in cyber-physical system for mobile robot to prevent disease

Nguyễn

Nguyen

Ngo

2023

International Journal of Advanced Robotic Systems

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In this article, we propose the visual application of a navigation framework for a wheeled robot to disinfect surfaces. Since dynamic environments are complicated, advanced sensors are integrated into the hardware platform to enhance the navigation task. The 2D lidar UTM-30LX from Hokuyo attached to the front of the robot can cover a wide scanning area. To provide better results in laser scan matching, an inertial measurement unit was integrated into the robot’s body. The output of this combination feeds into a global costmap for monitoring and navigation. Additionally, incremental encoders that obtain high-resolution position data are connected to the rear wheels. The role of the positioning sensor is to identify the existing location of the robot in a local costmap. To detect the appearance of a human, a Kinect digital camera is fixed to the top of the robot. All feedback signals are combined in the host computer to navigate the autonomous robot. For disinfection missions, the robot must carry several ultraviolet lamps to autonomously patrol in unknown environments. To visualize the robot’s effectiveness, our approach was validated using both a virtual simulation and an experimental test. The contributions of this work are summarized as follows: (i) a structure for ultraviolet-based hardware was first established; (ii) the theoretical computations for the robot’s localization in the 3D workspace will play a fundamental role in further developments; and (iii) data fusion from advanced sensing devices was integrated to enable navigation in uncertain environments.

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Section: Introductionmentioning

confidence: 99%

Visual application of navigation framework in cyber-physical system for mobile robot to prevent disease

Nguyễn

Nguyen

Ngo

2023

International Journal of Advanced Robotic Systems

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“…The artificial compound eye (ACE) [6] can surpass the visual task of the traditional imaging system by imitating the characteristics of the insect compound eye in some specific environments [7,8]. Inspired by this phenomenon, researchers conducted experiments on optical flow field estimation based on ACE, which offers important research value for the realization of accurate navigation and obstacle avoidance of unmanned aerial vehicle (UAV) and unmanned ground vehicle (UGV) [9][10][11].…”

Section: Introductionmentioning

confidence: 99%

Multi-aperture optical flow estimation for artificial compound eye using non-subsampled contourlet transform and guided filter

Zhao¹,

Wang²,

Liu³

et al. 2022

Meas. Sci. Technol.

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In this article, a multi-scale optical flow estimation for image taken by artificial compound eye (ACE) is investigated. The optical flow estimation of ACE needs to be adapted by designing algorithms according to its unique multi-aperture characteristics A more general filter for the regularization term, rather than a single iterative solution in the traditional variational model, is devised using the non-subsampled contourlet transform (NSCT) to enforce band-decomposition to estimate optical flow field. To circumvent the spillover and error of the single-aperture fringe flow field, a flow gradient weight is introduced to suppress it and to enhance motion details. Additionally, low-pass subbands adopt Bayes threshold operation with the advantage of efficiently eliminating outliers, and more high-pass subbands adopt guided filter (GF) with the advantage of separating important details from outliers. The prominent feature of the proposed method is that the accuracy of optical flow estimation is improved effectively by eliminating outliers. Eventually, the superiority of explored optical flow estimation is demonstrated through experimental results.

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“…Another interesting application of the Kalman filter is the sensor fusion, which is based on the combination of redundant information from different sensors to minimize their errors and enhance their performance. In this sense, several works dealing with the integration of Inertial Navigation System and Global Navigation Satellite System (INS/GNSS) to enhance the accuracy of vehicular navigation can be found in literature [11]- [14]. In these works, the integration of INS and GPS enables to sufficiently exploit the individual advantages of both standalone sensor systems and obtains an optimistic solution.…”

Section: Introductionmentioning

confidence: 99%

Estimation of Lateral Track Irregularity Through Kalman Filtering Techniques

et al. 2021

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The aim of this work is to develop a model-based methodology for monitoring lateral track irregularities based on the use of inertial sensors mounted on an in-service train. To this end, a gyroscope is used to measure the wheelset yaw angular velocity and two accelerometers are used to measure lateral acceleration of the wheelset and the bogie frame. The main contribution of the present work is the development of a very efficient Kalman-based monitoring strategy to estimate the lateral track irregularities. The Kalman filter is based on a highly simplified linear bogie model that is able to capture the most relevant dynamic behaviour of the vehicle. The behaviour of the designed filter is assessed through the use of a detailed multibody model of an in-service vehicle running on a straight track with realistic irregularities. The model output is used to generate virtual measurements that are subsequently used to run the filter and validate the proposed estimator. In addition, the equivalent parameters of the simplified model are identified based on these simulations. In order to prove the robustness of the proposed technique, a systematic parametric analysis has been performed. The results obtained with the proposed method are promising, showing high accuracy and robustness for monitoring lateral alignment on straight tracks, with a very low computational cost.INDEX TERMS Kalman filters, railway engineering, vehicle dynamics, railway safety, track irregularities, track surveying.

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Seamless integration of polarization compass and inertial navigation data with a self-learning multi-rate residual correction algorithm

Cited by 38 publications

References 30 publications

Visual application of navigation framework in cyber-physical system for mobile robot to prevent disease

Visual application of navigation framework in cyber-physical system for mobile robot to prevent disease

Multi-aperture optical flow estimation for artificial compound eye using non-subsampled contourlet transform and guided filter

Estimation of Lateral Track Irregularity Through Kalman Filtering Techniques

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