Introduction to Multisensor Fusion

What is/are Multisensor Fusion?

Multisensor Fusion - Third, we propose a resilient multisensor fusion method based on an optimization framework to fuse the UWB, visual and inertial measurements in a tightly coupled manner. ^[1] Accurate and robust calibration is crucial to a multisensor fusion-based system. ^[2] The method is further used in locating and diagnosing multisensor fusion faults. ^[3] Multisensor fusion-based road segmentation plays an important role in the intelligent driving system since it provides a drivable area. ^[4] This work answers the following questions: What are the publication characteristics and most influential publication sources? Who are the most active and influential authors? What are their research interests and primary contributions to society? What are the featured key studies in the field? What are the most popular topics and research trends, described by keywords? Additionally, we closely investigate renowned works that use different multisensor fusion methods, which are believed to be the bases of upcoming research. ^[5] The improvements of UAS in monitoring crop biomass in recent years are introduced, and multisensor fusion, multi-index fusion, the consideration of features not directly related to monitoring biomass, the adoption of advanced algorithms and the use of low-cost sensors are reviewed to highlight the potential for monitoring crop biomass with UAS. ^[6] In this paper, a multisensor fusion method based on the sparse representation is proposed, and the unsupervised Bayesian-based online diagnostic technique is adopted for water leakage detection in the deep manned submersible. ^[7] This paper applies multisensor fusion technology to predict tool wear. ^[8] In model-1, a new local multisensor fusion triage algorithm known as three-level localisation triage (3LLT) is proposed. ^[9] The position and orientation of the radar must be known in order to transform the detections from the radar coordinate system to a vehicle coordinate system (VCS), which is a common requirement for multisensor fusion. ^[10] The UAV and UGV are teamed such that the localization of the UAV is conducted on the UGV via the multisensor fusion of a fisheye camera, 3-D light detection and ranging, ranging radio, and a laser altimeter. ^[11] The idea of using ensemble deep learning algorithms through a multisensor fusion system can improve the performance of data fusion tasks. ^[12] The perception system of unmanned vehicles has developed rapidly in recent years, but in the process of multisensor fusion, a certain sensor's perception error often occurs which causes great errors in the perception results of the entire multi-sensor system and is harmful to human life and property. ^[13] To facilitate and quickly verify the multisensor fusion algorithm, a simulation platform for the intelligent vehicle and the experimental environment is built based on Gazebo software, which can realize the sensor data acquisition and the control decision function of the intelligent vehicle. ^[14] For improving the automation degree of PTL robots, current problems of key techniques, such as multisensor fusion and the establishment of datasets, are discussed and the prospect of inspection robots is presented. ^[15] In order to solve this problem, the solution proposed in this paper adopts multi-sensor data fusion technology, uses laser scanners and depth cameras to detect pedestrians, and establishes a pedestrian motion model to realize human body recognition and motion prediction based on multisensor fusion. ^[16] Consequently, in this article, an adaptive multisensor fusion methodology is proposed for hand pose estimation with two Leap Motions. ^[17] Experiments are conducted to verify the effectiveness of the proposed multisensor fusion strategy in accurate reconstruction of complex surfaces with high efficiency. ^[18] The performance of existing methods for multisensor fusion are severely affected by the lack of significant amount of labeled data. ^[19] A multisensor fusion algorithm is proposed to estimate the swing angle of interactive music. ^[20] In recent years, with the rapid development of multisensor fusion technology, point clouds used are no longer limited to those including 3-D coordinates acquired by visual sensors, such as binocular sensors or structured light sensors. ^[21] METHODS: The developed system is based on the multisensor fusion approach of detection which combined three techniques, namely: a source of laser light, a camera and an ultrasonic sensor. ^[22] It can be concluded that CNNs are suitable for multisensor fusion even with sensors that differ in resolutions by large factors, such as in the case of Sentinel-1 SAR and AMSR2. ^[23] This is achieved using a multisensor fusion approach that simultaneously collected urban environmental factors (e. ^[24] Multisensor fusion is required for precise defect characterization. ^[25] On the one hand, we tested the positioning and navigation functions of the omnidirectional mobile robot, and on the other hand, we verified the feasibility of positioning and navigation algorithms and multisensor fusion algorithms. ^[26] The experimental flight test results show that the proposed algorithm achieves a higher state solution accuracy and a better convergent performance compared with some conventional multisensor fusion algorithms. ^[27] A multisensor fusion between Kinect camera and inertial sensors was developed to animate a 3-D avatar during rehabilitation and to estimate kinematic data of different joints for clinical monitoring. ^[28] By modeling attacks as signals added to measurements with a certain probability, the attack model has been presented and incorporated into the existing process and measurement equations of ground vehicle pose estimation based on multisensor fusion. ^[29] We propose a remote sensing multisensor fusion system using optical data including Landsat-8 and Sentinel-2 and RADAR data including Sentinel-1 for water body extraction. ^[30] Target detection, target recognition, and multisensor fusion are analyzed in the optimized part of sensor results. ^[31] Multisensor fusion-based approaches are usually used to improve detection performance. ^[32] Finally, we present emerging opportunities to address the relatively coarse resolution of ABI observations through multisensor fusion to resolve landscape heterogeneity and to leverage observations from ABI to study the carbon cycle and ecosystem function at unprecedented temporal frequency. ^[33] Therefore, this paper proposes a respiratory monitoring method based on a BP neural network combined with multisensor fusion technology. ^[34] This paper presents a deep-learning-based model termed multiresolution & multisensor fusion network for motor fault diagnosis, through multiscale analysis of motor vibration and stator current signals. ^[35] This paper investigates multisensor fusion in the setting of Gaussian process estimation for nonlinear dynamic systems. ^[36] The localization problem can be solved by fusing the information from multiple sensors, and the multisensor fusion problem is typically solved using probabilistic methods. ^[37] Therefore, this paper presents a spatiotemporal and kinetic gait analysis system using a single LRS and instrumented insoles and proposes a multisensor fusion algorithm for tracking leg motions. ^[38] We demonstrate a portable multisensor fusion system for the automated analysis of multiple beverages. ^[39] In this article, we present a multisensor fusion framework for ego and adjacent lanes with a novel fusion quality measure and dynamic lane mode strategies for erroneous management. ^[40] To overcome the existing limitations in the identification and characterization of sand particles with different concentration and size in multiphase flows, a vibration multisensor fusion method with three specially selected high-frequency vibration sensors for the detection of minuscule sand particles was evaluated. ^[41] A robust and scalable 360◦ multisensor fusion framework for static and dynamic obstacles in conjunction with a sensor-based erroneous management block is proposed. ^[42] Specifically, we propose a decision-level multisensor fusion technique for semantic labeling of the very-high-resolution optical imagery and LiDAR data. ^[43] This paper presents a multisensor fusion approach between IMU and ground optical flow used to estimate the position of a mobile robot while ensuring high integrity localization. ^[44] Aiming at high performance requirements of snake-like robots under complex environment, we present a control system of our proposed design which utilizes a STM32 as the core processor and incorporates real-time image acquisition, multisensor fusion, and wireless communication technology. ^[45] In this paper, we propose thermal multisensor fusion (TMF) for collaborative robots to overcome the limitations of stand-alone robots. ^[46] The computation complexity of these relaxation methods are also discussed, and the equivalent result is helpful to set membership filter and multisensor fusion obtaining a tighter ellipsoid to contain the state with lower computation complexity. ^[47] A recent trend in distributed multisensor fusion is to use random finite-set filters at the sensor nodes and fuse the filtered distributions algorithmically using their exponential mixture densities (EMDs). ^[48] Multiresolution and multisensor fusion are jointly achieved through an explicitly hierarchical probabilistic graphical classifier, which uses a quadtree structure to model the interactions across different spatial resolutions, and a symmetric Markov mesh random field to deal with contextual information at each scale and favor applicability to very high resolution imagery. ^[49] Therefore, in order to accurately control the exoskeleton movement, a multisensor fusion gait recognition system was developed in this study. ^[50]

multisensor fusion algorithm

To facilitate and quickly verify the multisensor fusion algorithm, a simulation platform for the intelligent vehicle and the experimental environment is built based on Gazebo software, which can realize the sensor data acquisition and the control decision function of the intelligent vehicle. ^[1] A multisensor fusion algorithm is proposed to estimate the swing angle of interactive music. ^[2] On the one hand, we tested the positioning and navigation functions of the omnidirectional mobile robot, and on the other hand, we verified the feasibility of positioning and navigation algorithms and multisensor fusion algorithms. ^[3] The experimental flight test results show that the proposed algorithm achieves a higher state solution accuracy and a better convergent performance compared with some conventional multisensor fusion algorithms. ^[4] Therefore, this paper presents a spatiotemporal and kinetic gait analysis system using a single LRS and instrumented insoles and proposes a multisensor fusion algorithm for tracking leg motions. ^[5]

multisensor fusion method

Third, we propose a resilient multisensor fusion method based on an optimization framework to fuse the UWB, visual and inertial measurements in a tightly coupled manner. ^[1] This work answers the following questions: What are the publication characteristics and most influential publication sources? Who are the most active and influential authors? What are their research interests and primary contributions to society? What are the featured key studies in the field? What are the most popular topics and research trends, described by keywords? Additionally, we closely investigate renowned works that use different multisensor fusion methods, which are believed to be the bases of upcoming research. ^[2] In this paper, a multisensor fusion method based on the sparse representation is proposed, and the unsupervised Bayesian-based online diagnostic technique is adopted for water leakage detection in the deep manned submersible. ^[3] To overcome the existing limitations in the identification and characterization of sand particles with different concentration and size in multiphase flows, a vibration multisensor fusion method with three specially selected high-frequency vibration sensors for the detection of minuscule sand particles was evaluated. ^[4]

multisensor fusion technology

This paper applies multisensor fusion technology to predict tool wear. ^[1] In recent years, with the rapid development of multisensor fusion technology, point clouds used are no longer limited to those including 3-D coordinates acquired by visual sensors, such as binocular sensors or structured light sensors. ^[2] Therefore, this paper proposes a respiratory monitoring method based on a BP neural network combined with multisensor fusion technology. ^[3]

multisensor fusion system

The idea of using ensemble deep learning algorithms through a multisensor fusion system can improve the performance of data fusion tasks. ^[1] We propose a remote sensing multisensor fusion system using optical data including Landsat-8 and Sentinel-2 and RADAR data including Sentinel-1 for water body extraction. ^[2] We demonstrate a portable multisensor fusion system for the automated analysis of multiple beverages. ^[3]

multisensor fusion approach

METHODS: The developed system is based on the multisensor fusion approach of detection which combined three techniques, namely: a source of laser light, a camera and an ultrasonic sensor. ^[1] This is achieved using a multisensor fusion approach that simultaneously collected urban environmental factors (e. ^[2] This paper presents a multisensor fusion approach between IMU and ground optical flow used to estimate the position of a mobile robot while ensuring high integrity localization. ^[3]

multisensor fusion framework

In this article, we present a multisensor fusion framework for ego and adjacent lanes with a novel fusion quality measure and dynamic lane mode strategies for erroneous management. ^[1] A robust and scalable 360◦ multisensor fusion framework for static and dynamic obstacles in conjunction with a sensor-based erroneous management block is proposed. ^[2]