Disparity estimation for multi-scale multi-sensor fusion

The perception module of advanced driver assistance systems plays a vital role.Perception schemes often use a single sensor for data processing and environmental perception or adopt the information processing results of various sensors for the fusion of the detection layer.This paper proposes a multi-scale and multi-sensor data fusion strategy in the front end of perception and accomplishes a multi-sensor function disparity map generation scheme.A binocular stereo vision sensor composed of two cameras and a light deterction and ranging(LiDAR)sensor is used to jointly perceive the environment,and a multi-scale fusion scheme is employed to improve the accuracy of the disparity map.This solution not only has the advantages of dense perception of binocular stereo vision sensors but also considers the perception accuracy of LiDAR sensors.Experiments demonstrate that the multi-scale multi-sensor scheme proposed in this paper significantly improves disparity map estimation.

Research on the Mechanism of Multi-Sensor Fusion Configuration Based on the Optimal Principle of the Vehicle

In order to address the issue of sensor configuration redundancy in intelligent driving,this paper constructs a multi-objective optimization model that considers cost,coverage ability,and perception performance.And then,combining a specific set of parameters,the NSGA-II algorithm is used to solve the multi-objective model established in this paper,and a Pareto front containing 24 typical configuration schemes is extracted after considering empirical constraints.Finally,using the decision preference method proposed in this paper that combines subjective and objective factors,decision scores are calculated and ranked for various configuration schemes from both cost and performance preferences.The research results indicate that the multi-objective optimization model established in this paper can screen and optimize various configuration schemes from the optimal principle of the vehicle,and the optimized configuration schemes can be quantitatively ranked to obtain the decision results for the vehicle under different preference tendencies.

State Estimation Method for GNSS/INS/Visual Multi-sensor Fusion Based on Factor Graph Optimization for Unmanned System

With the development of unmanned driving technology,intelligent robots and drones,high-precision localization,navigation and state estimation technologies have also made great progress.Traditional global navigation satellite system/inertial navigation system(GNSS/INS)integrated navigation systems can provide high-precision navigation information continuously.However,when this system is applied to indoor or GNSS-denied environments,such as outdoor substations with strong electromagnetic interference and complex dense spaces,it is often unable to obtain high-precision GNSS positioning data.The positioning and orientation errors will diverge and accumulate rapidly,which cannot meet the high-precision localization requirements in large-scale and long-distance navigation scenarios.This paper proposes a method of high-precision state estimation with fusion of GNSS/INS/Vision using a nonlinear optimizer factor graph optimization as the basis for multi-source optimization.Through the collected experimental data and simulation results,this system shows good performance in the indoor environment and the environment with partial GNSS signal loss.

High-precision urban rail map construction based on multi-sensor fusion

The construction of high-precision urban rail maps is crucial for the safe and efficient operation of railway transportation systems.However,the repetitive features and sparse textures in urban rail environments pose challenges for map construction with high-precision.Motivated by this,this paper proposes a high-precision urban rail map construction algorithm based on multi-sensor fusion.The algorithm integrates laser radar and Inertial Measurement Unit(IMU)data to construct the geometric structure map of the urban rail.It utilizes image point-line features and color information to improve map accuracy by minimizing photometric errors and incorporating color information,thus generating high-precision maps.Experimental results on a real urban rail dataset demonstrate that the proposed algorithm achieves root mean square errors of 0.345 and 1.033m for ground and tunnel scenes,respectively,representing a 19.31%and 56.80%improvement compared to state-ofthe-art methods.

Subsurface Temperature and Salinity Structures Inversion Using a Stacking-Based Fusion Model from Satellite Observations in the South China Sea

Three-dimensional ocean subsurface temperature and salinity structures(OST/OSS)in the South China Sea(SCS)play crucial roles in oceanic climate research and disaster mitigation.Traditionally,real-time OST and OSS are mainly obtained through in-situ ocean observations and simulation by ocean circulation models,which are usually challenging and costly.Recently,dynamical,statistical,or machine learning models have been proposed to invert the OST/OSS from sea surface information;however,these models mainly focused on the inversion of monthly OST and OSS.To address this issue,we apply clustering algorithms and employ a stacking strategy to ensemble three models(XGBoost,Random Forest,and LightGBM)to invert the real-time OST/OSS based on satellite-derived data and the Argo dataset.Subsequently,a fusion of temperature and salinity is employed to reconstruct OST and OSS.In the validation dataset,the depth-averaged Correlation(Corr)of the estimated OST(OSS)is 0.919(0.83),and the average Root-Mean-Square Error(RMSE)is0.639°C(0.087 psu),with a depth-averaged coefficient of determination(R~2)of 0.84(0.68).Notably,at the thermocline where the base models exhibit their maximum error,the stacking-based fusion model exhibited significant performance enhancement,with a maximum enhancement in OST and OSS inversion exceeding 10%.We further found that the estimated OST and OSS exhibit good agreement with the HYbrid Coordinate Ocean Model(HYCOM)data and BOA_Argo dataset during the passage of a mesoscale eddy.This study shows that the proposed model can effectively invert the real-time OST and OSS,potentially enhancing the understanding of multi-scale oceanic processes in the SCS.

Weighted Multi-sensor Data Level Fusion Method of Vibration Signal Based on Correlation Function

As the differences of sensor＇s precision and some random factors are difficult to control,the actual measurement signals are far from the target signals that affect the reliability and precision of rotating machinery fault diagnosis.The traditional signal processing methods,such as classical inference and weighted averaging algorithm usually lack dynamic adaptability that is easy for trends to cause the faults to be misjudged or left out.To enhance the measuring veracity and precision of vibration signal in rotary machine multi-sensor vibration signal fault diagnosis,a novel data level fusion approach is presented on the basis of correlation function analysis to fast determine the weighted value of multi-sensor vibration signals.The approach doesn＇t require knowing the prior information about sensors,and the weighted value of sensors can be confirmed depending on the correlation measure of real-time data tested in the data level fusion process.It gives greater weighted value to the greater correlation measure of sensor signals,and vice versa.The approach can effectively suppress large errors and even can still fuse data in the case of sensor failures because it takes full advantage of sensor＇s own-information to determine the weighted value.Moreover,it has good performance of anti-jamming due to the correlation measures between noise and effective signals are usually small.Through the simulation of typical signal collected from multi-sensors,the comparative analysis of dynamic adaptability and fault tolerance between the proposed approach and traditional weighted averaging approach is taken.Finally,the rotor dynamics and integrated fault simulator is taken as an example to verify the feasibility and advantages of the proposed approach,it is shown that the multi-sensor data level fusion based on correlation function weighted approach is better than the traditional weighted average approach with respect to fusion precision and dynamic adaptability.Meantime,the approach is adaptable and easy to use,can be applied to other areas of vibration measurement.

Data Fusion in Distributed Multi-sensor System

This paper presents a data fusion method in distributed multi-sensor system including GPS and INS sensors’ data processing. First, a residual χ 2 \|test strategy with the corresponding algorithm is designed. Then a coefficient matrices calculation method of the information sharing principle is derived. Finally, the federated Kalman filter is used to combine these independent, parallel, real\|time data. A pseudolite (PL) simulation example is given.

STUDY ON THE COAL-ROCK INTERFACE RECOGNITION METHOD BASED ON MULTI-SENSOR DATA FUSION TECHNIQUE

The coal-rock interface recognition method based on multi-sensor data fusiontechnique is put forward because of the localization of single type sensor recognition method. Themeasuring theory based on multi-sensor data fusion technique is analyzed, and hereby the testplatform of recognition system is manufactured. The advantage of data fusion with the fuzzy neuralnetwork (FNN) technique has been probed. The two-level FNN is constructed and data fusion is carriedout. The experiments show that in various conditions the method can always acquire a much higherrecognition rate than normal ones.

A Novel Multi-sensor Data Fusion Algorithm and Its Application to Diagnostics

To Meet the requirements of multi-sensor data fusion in diagnosis for complex equipment systems,a novel, fuzzy similarity-based data fusion algorithm is given. Based on fuzzy set theory, it calculates the fuzzy similarity among a certain sensor's measurement values and the multiple sensor's objective prediction values to determine the importance weigh of each sensor,and realizes the multi-sensor diagnosis parameter data fusion.According to the principle, its application software is also designed. The applied example proves that the algorithm can give priority to the high-stability and high -reliability sensors and it is laconic ,feasible and efficient to real-time circumstance measure and data processing in engine diagnosis.

A Generic Plug-and-Play Navigation Fusion Strategy for Land Vehicles in GNSS-Denied Environment

Achieving accurate navigation information by integrating multiple sensors is key to the safe operation of land vehicles in global navigation satellite system(GNSS)-denied environment.However,current multi-sensor fusion methods are based on stovepipe architecture,which is optimized with custom fusion strategy for specific sensors.Seeking to develop adaptable navigation that allows rapid integration of any combination of sensors to obtain robust and high-precision navigation solutions in GNSS-denied environment,we propose a generic plug-and-play fusion strategy to estimate land vehicle states.The proposed strategy can handle different sensors in a plug-and-play manner as sensors are abstracted and represented by generic models,which allows rapid reconfiguration whenever a sensor signal is additional or lost during operation.Relative estimations are fused with absolute sensors based on improved factor graph,which includes sensors’error parameters in the non-linear optimization process to conduct sensor online calibration.We evaluate the performance of our approach using a land vehicle equipped with a global positioning system(GPS)receiver as well as inertial measurement unit(IMU),camera,wireless sensor and odometer.GPS is not integrated into the system but treated as ground truth.Results are compared with the most common filtering-based fusion algorithm.It shows that our strategy can process low-quality input sources in a plug-and-play and robust manner and its performance outperforms filtering-based method in GNSS-denied environment.

Multi-sensor measurement and data fusion technology for manufacturing process monitoring:a literature review

Due to the rapid development of precision manufacturing technology,much research has been conducted in the field of multisensor measurement and data fusion technology with a goal of enhancing monitoring capabilities in terms of measurement accuracy and information richness,thereby improving the efficiency and precision of manufacturing.In a multisensor system,each sensor independently measures certain parameters.Then,the system uses a relevant signalprocessing algorithm to combine all of the independent measurements into a comprehensive set of measurement results.The purpose of this paper is to describe multisensor measurement and data fusion technology and its applications in precision monitoring systems.The architecture of multisensor measurement systems is reviewed,and some implementations in manufacturing systems are presented.In addition to the multisensor measurement system,related data fusion methods and algorithms are summarized.Further perspectives on multisensor monitoring and data fusion technology are included at the end of this paper.

Multimodal Sentiment Analysis Using BiGRU and Attention-Based Hybrid Fusion Strategy

Recently,multimodal sentiment analysis has increasingly attracted attention with the popularity of complementary data streams,which has great potential to surpass unimodal sentiment analysis.One challenge of multimodal sentiment analysis is how to design an efficient multimodal feature fusion strategy.Unfortunately,existing work always considers feature-level fusion or decision-level fusion,and few research works focus on hybrid fusion strategies that contain feature-level fusion and decision-level fusion.To improve the performance of multimodal sentiment analysis,we present a novel multimodal sentiment analysis model using BiGRU and attention-based hybrid fusion strategy(BAHFS).Firstly,we apply BiGRU to learn the unimodal features of text,audio and video.Then we fuse the unimodal features into bimodal features using the bimodal attention fusion module.Next,BAHFS feeds the unimodal features and bimodal features into the trimodal attention fusion module and the trimodal concatenation fusion module simultaneously to get two sets of trimodal features.Finally,BAHFS makes a classification with the two sets of trimodal features respectively and gets the final analysis results with decision-level fusion.Based on the CMU-MOSI and CMU-MOSEI datasets,extensive experiments have been carried out to verify BAHFS’s superiority.

Applications of state estimation in multi-sensor information fusion for the monitoring of open pit mine slope deformation

The traditional open pit mine slope deformation monitoring system can not use the monitoring information coming from many monitoring points at the same time, can only using the monitoring data coming from a key monitoring point,and that is to say it can only handle one-dimensional time series.Given this shortage in the monitoring, the multi-sensor information fusion in the state estimation techniques would be intro- duced to the slope deformation monitoring system,and by the dynamic characteristics of deformation slope,the open pit slope would be regarded as a dynamic goal,the condi- tion monitoring of which would be regarded as a dynamic target tracking.Distributed In- formation fusion technology with feedback was used to process the monitoring data and on this basis Klman filtering algorithms was introduced,and the simulation examples was used to prove its effectivenes.

An Indoor Pedestrian Localization Algorithm Based on Multi-Sensor Information Fusion

For existing indoor localization algorithm has low accuracy, high cost in deployment and maintenance, lack of robustness, and low sensor utilization, this paper proposes a particle filter algorithm based on multi-sensor fusion. The pedestrian’s localization in indoor environment is described as dynamic system state estimation problem. The algorithm combines the smart mobile terminal with indoor localization, and filters the result of localization with the particle filter. In this paper, a dynamic interval particle filter algorithm based on pedestrian dead reckoning (PDR) information and RSSI localization information have been used to improve the filtering precision and the stability. Moreover, the localization results will be uploaded to the server in time, and the location fingerprint database will be built incrementally, which can adapt the dynamic changes of the indoor environment. Experimental results show that the algorithm based on multi-sensor improves the localization accuracy and robustness compared with the location algorithm based on Wi-Fi.

A Study of Multi-sensor Data Fusion System Based on MAS for Nutrient Solution Measurement

For complementarity and redundancy of multi-sensor data fusion (MSDF) system,it is an effective approach for multiple components measurement.In order to measure nutrient solution on-line,a dynamic and complex system under greenhouse environment,sensors should have intelligent properties including self-calibration and self-compensation. Meanwhile,it is necessary for multiple sensors to cooperate and interact for enhancing reliability of multi-sensor system. Because of the properties of multi-agent system (MAS),it is an appropriate tool to study MSDF system.This paper proposed an architecture of MSDF system based on MAS for the multiple components measurement of nutrient solution.The sensor agent's structure and function modules are analyzed and described in detail,the formal definitions are given,too.The relations of the sensors are modeled to implement reliability diagnosis of the multi-sensor system,so that the reliability of nutrient control system is enhanced.This study offers an effective approach for the study of MSDF.

Multi-Sensor Image Fusion: A Survey of the State of the Art

Image fusion has been developing into an important area of research. In remote sensing, the use of the same image sensor in different working modes, or different image sensors, can provide reinforcing or complementary information. Therefore, it is highly valuable to fuse outputs from multiple sensors (or the same sensor in different working modes) to improve the overall performance of the remote images, which are very useful for human visual perception and image processing task. Accordingly, in this paper, we first provide a comprehensive survey of the state of the art of multi-sensor image fusion methods in terms of three aspects: pixel-level fusion, feature-level fusion and decision-level fusion. An overview of existing fusion strategies is then introduced, after which the existing fusion quality measures are summarized. Finally, this review analyzes the development trends in fusion algorithms that may attract researchers to further explore the research in this field.

Multi-sensor Data Fusion by Improved Hough Transformation

In this paper we present an evidence-gathering approach to slove the multi-sensor data fusion problem. It uses an improved Hough transformation method rather than the usual statistical or geometric approach to extract the directions and positions of the walls in a room and update the location (orientation and position)of a mobile robot. The simulation results show that the proposed method is of practical importance since it is very simple and easy to implement.

Obstacle avoidance technology of bionic quadruped robot based on multi-sensor information fusion

In order to improve the ability of a bionic quadruped robot to percept the location of obstacles in a complex and dynamic environment, the information fusion between an ultrasonic sensor and a binocular sensor was studied under the condition that the robot moves in the Walk gait on a structured road. Firstly, the distance information of obstacles from these two sensors was separately processed by the Kalman filter algorithm, which largely reduced the noise interference. After that, we obtained two groups of estimated distance values from the robot to the obstacle and a variance of the estimation value. Additionally, a fusion of the estimation values and the variances was achieved based on the STF fusion algorithm. Finally, a simulation was performed to show that the curve of a real value was tracked well by that of the estimation value, which attributes to the effectiveness of the Kalman filter algorithm. In contrast to statistics before fusion, the fusion variance of the estimation value was sharply decreased. The precision of the position information is 4. 6 cm, which meets the application requirements of the robot.

Progress and Achievements of Multi-sensor Fusion Navigation in China during 2019—2023

Global Navigation Satellite System(GNSS)can provide all-weather,all-time,high-precision positioning,navigation and timing services,which plays an important role in national security,national economy,public life and other aspects.However,in environments with limited satellite signals such as urban canyons,tunnels,and indoor spaces,it is difficult to provide accurate and reliable positioning services only by satellite navigation.Multi-source sensor integrated navigation can effectively overcome the limitations of single-sensor navigation through the fusion of different types of sensor data such as Inertial Measurement Unit(IMU),vision sensor,and LiDAR,and provide more accurate,stable and robust navigation information in complex environments.We summarizes the research status of multi-source sensor integrated navigation technology,and focuses on the representative innovations and applications of integrated navigation and positioning technology by major domestic scientific research institutions in China during 2019—2023.

Research on Optimal Preload Method of Controllable Rolling Bearing Based on Multisensor Fusion

Angular contact ball bearings have been widely used in machine tool spindles,and the bearing preload plays an important role in the performance of the spindle.In order to solve the problems of the traditional optimal preload prediction method limited by actual conditions and uncertainties,a roller bearing preload test method based on the improved D-S evidence theorymulti-sensor fusion method was proposed.First,a novel controllable preload system is proposed and evaluated.Subsequently,multiple sensors are employed to collect data on the bearing parameters during preload application.Finally,a multisensor fusion algorithm is used to make predictions,and a neural network is used to optimize the fitting of the preload data.The limitations of conventional preload testing methods are identified,and the integration of complementary information frommultiple sensors is used to achieve accurate predictions,offering valuable insights into the optimal preload force.Experimental results demonstrate that the multi-sensor fusion approach outperforms traditional methods in accurately measuring the optimal preload for rolling bearings.