An Outline of Multi-Sensor Fusion Methods for Mobile Agents Indoor Navigation

Qu, Yuanhao; Yang, Minghao; Zhang, Jiaqing; Xie, Wu; Qiang, Baohua; Chen, Jinlong

doi:10.3390/s21051605

Cited by 24 publications

(14 citation statements)

References 133 publications

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“…Although there is no accumulated error in the acceleration data of IMU used in this paper, there are still errors caused by static drift and poor dynamic response (Fan et al, 2019;Qu et al, 2021). The static drift of the IMU is slightly different in each movement process, and the designed robot is a step-by-step movement combined with dynamic and static, and the sensor needs to make a dynamic response for each movement step.…”

Section: Compensating Sensor Defects With Bpnnmentioning

confidence: 97%

“…With the research and development of mobile robots and various sensors (Kwon & Yi, 2012; Liu et al, 2020), three‐dimensional (3D) dead reckoning systems based on multisensor data fusion (MDF) technology (J. Dong et al, 2009; Qu et al, 2021) are more and more developed and applied to pipeline mobile robots, so as to realize the dead reckoning inside the pipeline more conveniently, efficiently, and accurately. It can be seen that how to accurately complete the 3D dead reckoning of the pipeline mobile robot has become an urgent problem to be solved.…”

Section: Introductionmentioning

confidence: 99%

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Three‐dimensional dead reckoning of wall‐climbing robot based on information fusion of compound extended Kalman filter

Wei

Zhang

2022

Journal of Field Robotics

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Dead reckoning accuracy determines the operation quality of pipeline mobile robot to a certain extent. With the progress of the industry, the research on the dead reckoning system of pipeline robot is more and more in-depth, but how to improve dead reckoning accuracy is still a difficult problem for pipeline mobile robot. In this paper, to overcome the sensor defects of the traditional dead reckoning system, an error backpropagation neural network (BPNN) is introduced to compensate the sensor error caused by static drift and poor dynamic response. To reduce the interference of noise information, the covariance matching technique based on the Adaptive Neuro-Fuzzy Inference System is used to estimate the measurement noise information. And then the two are combined in the algorithm based on the extended Kalman Filter (EKF) framework for data fusion. An EKF algorithm based on adaptive neural fuzzy combined with BPNN is proposed. Finally, a complete three-dimensional dead reckoning system is established by coordinate transformation. The motion experiment of the quadruped wall-climbing robot in the pipeline proves that the proximity L of the calculated trajectory is not more than 6.00%, and the lowest is 1.07%, which effectively improves the reliability, robustness, and positioning accuracy of the dead reckoning.

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Section: Compensating Sensor Defects With Bpnnmentioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%

Three‐dimensional dead reckoning of wall‐climbing robot based on information fusion of compound extended Kalman filter

Wei

Zhang

2022

Journal of Field Robotics

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“…The taxonomy of sensor fusion methods for mobile robot odometry is dependent on their working principle, and adopted from recent surveys on sensor fusion [ 10 , 248 , 251 , 252 ]. Filter-based methods [ 253 ] and optimization-based methods [ 254 , 255 ] are summarized below.…”

Section: Sensor Fusionmentioning

confidence: 99%

Sensors and Sensor Fusion Methodologies for Indoor Odometry: A Review

et al. 2022

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Although Global Navigation Satellite Systems (GNSSs) generally provide adequate accuracy for outdoor localization, this is not the case for indoor environments, due to signal obstruction. Therefore, a self-contained localization scheme is beneficial under such circumstances. Modern sensors and algorithms endow moving robots with the capability to perceive their environment, and enable the deployment of novel localization schemes, such as odometry, or Simultaneous Localization and Mapping (SLAM). The former focuses on incremental localization, while the latter stores an interpretable map of the environment concurrently. In this context, this paper conducts a comprehensive review of sensor modalities, including Inertial Measurement Units (IMUs), Light Detection and Ranging (LiDAR), radio detection and ranging (radar), and cameras, as well as applications of polymers in these sensors, for indoor odometry. Furthermore, analysis and discussion of the algorithms and the fusion frameworks for pose estimation and odometry with these sensors are performed. Therefore, this paper straightens the pathway of indoor odometry from principle to application. Finally, some future prospects are discussed.

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“…Multi-sensor information fusion [41][42][43][44][45][46][47][48][49][50] analyzes and processes the multi-source information collected by sensors and combines them. The combination of multi-source information can be automatically or semi-automatically carried out [51][52][53][54]. In the fusion process of faces images, some valuable information for face recognition is possibly lost and in turn, can generate a more challenging dataset for us.…”

Section: Introductionmentioning

confidence: 99%

Broad Learning System for Tackling Emerging Challenges in Face Recognition

Zhang¹,

Wang²

2023

Computer Modeling in Engineering &Amp; Sciences

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Face recognition has been rapidly developed and widely used. However, there is still considerable uncertainty in the computational intelligence based on human-centric visual understanding. Emerging challenges for face recognition are resulted from information loss. This study aims to tackle these challenges with a broad learning system (BLS). We integrated two models, IR 3 C with BLS and IR 3 C with a triplet loss, to control the learning process. In our experiments, we used different strategies to generate more challenging datasets and analyzed the competitiveness, sensitivity, and practicability of the proposed two models. In the model of IR 3 C with BLS, the recognition rates for the four challenging strategies are all 100%. In the model of IR 3 C with a triplet loss, the recognition rates are 94.61%, 94.61%, 96.95%, 96.23%, respectively. The experiment results indicate that the proposed two models can achieve a good performance in tackling the considered information loss challenges from face recognition.

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An Outline of Multi-Sensor Fusion Methods for Mobile Agents Indoor Navigation

Cited by 24 publications

References 133 publications

Three‐dimensional dead reckoning of wall‐climbing robot based on information fusion of compound extended Kalman filter

Three‐dimensional dead reckoning of wall‐climbing robot based on information fusion of compound extended Kalman filter

Sensors and Sensor Fusion Methodologies for Indoor Odometry: A Review

Broad Learning System for Tackling Emerging Challenges in Face Recognition

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