Calibration Methods for Large-Scale and High-Precision Globalization of Local Point Cloud Data Based on iGPS.

The point cloud is one of the measurement results of local measurement and is widely used because of its high measurement accuracy, high data density, and low environmental impact. However, since point cloud data from a single measurement are generally small in spatial extent, it is necessary to accurately globalize the local point cloud to measure large components. In this paper, the method of using an iGPS (indoor Global Positioning System) as an external measurement device to realize high-accuracy globalization of local point cloud data is proposed. Two calibration models are also discussed for different application scenarios. Verification experiments prove that the average calibration errors of these two calibration models are 0.12 mm and 0.17 mm, respectively. The proposed method can maintain calibration precision in a large spatial range (about 10 m × 10 m × 5 m), which is of high value for engineering applications.

Robust Detection of Critical Events in the Context of Railway Security Based on Multimodal Sensor Data Fusion.

Effective security surveillance is crucial in the railway sector to prevent security incidents, including vandalism, trespassing, and sabotage. This paper discusses the challenges of maintaining seamless surveillance over extensive railway infrastructure, considering both technological advances and the growing risks posed by terrorist attacks. Based on previous research, this paper discusses the limitations of current surveillance methods, particularly in managing information overload and false alarms that result from integrating multiple sensor technologies. To address these issues, we propose a new fusion model that utilises Probabilistic Occupancy Maps (POMs) and Bayesian fusion techniques. The fusion model is evaluated on a comprehensive dataset comprising three use cases with a total of eight real life critical scenarios. We show that, with this model, the detection accuracy can be increased while simultaneously reducing the false alarms in railway security surveillance systems. This way, our approach aims to enhance situational awareness and reduce false alarms, thereby improving the effectiveness of railway security measures.

Autonomous Navigation by Mobile Robot with Sensor Fusion Based on Deep Reinforcement Learning.

In the domain of mobile robot navigation, conventional path-planning algorithms typically rely on predefined rules and prior map information, which exhibit significant limitations when confronting unknown, intricate environments. With the rapid evolution of artificial intelligence technology, deep reinforcement learning (DRL) algorithms have demonstrated considerable effectiveness across various application scenarios. In this investigation, we introduce a self-exploration and navigation approach based on a deep reinforcement learning framework, aimed at resolving the navigation challenges of mobile robots in unfamiliar environments. Firstly, we fuse data from the robot's onboard lidar sensors and camera and integrate odometer readings with target coordinates to establish the instantaneous state of the decision environment. Subsequently, a deep neural network processes these composite inputs to generate motion control strategies, which are then integrated into the local planning component of the robot's navigation stack. Finally, we employ an innovative heuristic function capable of synthesizing map information and global objectives to select the optimal local navigation points, thereby guiding the robot progressively toward its global target point. In practical experiments, our methodology demonstrates superior performance compared to similar navigation methods in complex, unknown environments devoid of predefined map information.

An Indoor Fire Detection Method Based on Multi-Sensor Fusion and a Lightweight Convolutional Neural Network.

Indoor fires pose significant threats in terms of casualties and economic losses globally. Thus, it is vital to accurately detect indoor fires at an early stage. To improve the accuracy of indoor fire detection for the resource-constrained embedded platform, an indoor fire detection method based on multi-sensor fusion and a lightweight convolutional neural network (CNN) is proposed. Firstly, the Savitzky-Golay (SG) filter is used to clean the three types of heterogeneous sensor data, then the cleaned sensor data are transformed by means of the Gramian Angular Field (GAF) method into matrices, which are finally integrated into a three-dimensional matrix. This preprocessing stage will preserve temporal dependency and enlarge the characteristics of time-series data. Therefore, we could reduce the number of blocks, channels and layers in the network, leading to a lightweight CNN for indoor fire detection. Furthermore, we use the Fire Dynamic Simulator (FDS) to simulate data for the training stage, enhancing the robustness of the network. The fire detection performance of the proposed method is verified through an experiment. It was found that the proposed method achieved an impressive accuracy of 99.1%, while the number of CNN parameters and the amount of computation is still small, which is more suitable for the resource-constrained embedded platform of an indoor fire detection system.

Leader-Follower Approach for Non-Holonomic Mobile Robots Based on Extended Kalman Filter Sensor Data Fusion and Extended On-Board Camera Perception Controlled with Behavior Tree.

This paper presents a leader-follower mobile robot control approach using onboard sensors. The follower robot is equipped with an Intel RealSense camera mounted on a rotating platform. Camera observations and ArUco markers are used to localize the robots to each other and relative to the workspace. The rotating platform allows the expansion of the perception range. As a result, the robot can use observations that are not within the camera's field of view at the same time in the localization process. The decision-making process associated with the control of camera rotation is implemented using behavior trees. In addition, measurements from encoders and IMUs are used to improve the quality of localization. Data fusion is performed using the EKF filter and allows the user to determine the robot's poses. A 3D-printed cuboidal tower is added to the leader robot with four ArUco markers located on its sides. Fiducial landmarks are placed on vertical surfaces in the workspace to improve the localization process. The experiments were performed to verify the effectiveness of the presented control algorithm. The robot operating system (ROS) was installed on both robots.

Multi-Sensor Data Fusion and CNN-LSTM Model for Human Activity Recognition System.

Human activity recognition (HAR) is becoming increasingly important, especially with the growing number of elderly people living at home. However, most sensors, such as cameras, do not perform well in low-light environments. To address this issue, we designed a HAR system that combines a camera and a millimeter wave radar, taking advantage of each sensor and a fusion algorithm to distinguish between confusing human activities and to improve accuracy in low-light settings. To extract the spatial and temporal features contained in the multisensor fusion data, we designed an improved CNN-LSTM model. In addition, three data fusion algorithms were studied and investigated. Compared to camera data in low-light environments, the fusion data significantly improved the HAR accuracy by at least 26.68%, 19.87%, and 21.92% under the data level fusion algorithm, feature level fusion algorithm, and decision level fusion algorithm, respectively. Moreover, the data level fusion algorithm also resulted in a reduction of the best misclassification rate to 2%~6%. These findings suggest that the proposed system has the potential to enhance the accuracy of HAR in low-light environments and to decrease human activity misclassification rates.

Real-Time Trajectory Prediction Method for Intelligent Connected Vehicles in Urban Intersection Scenarios.

Intelligent connected vehicles (ICVs) have played an important role in improving the intelligence degree of transportation systems, and improving the trajectory prediction capability of ICVs is beneficial for traffic efficiency and safety. In this paper, a real-time trajectory prediction method based on vehicle-to-everything (V2X) communication is proposed for ICVs to improve the accuracy of their trajectory prediction. Firstly, this paper applies a Gaussian mixture probability hypothesis density (GM-PHD) model to construct the multidimension dataset of ICV states. Secondly, this paper adopts vehicular microscopic data with more dimensions, which is output by GM-PHD as the input of LSTM to ensure the consistency of the prediction results. Then, the signal light factor and Q-Learning algorithm were applied to improve the LSTM model, adding features in the spatial dimension to complement the temporal features used in the LSTM. When compared with the previous models, more consideration was given to the dynamic spatial environment. Finally, an intersection at Fushi Road in Shijingshan District, Beijing, was selected as the field test scenario. The final experimental results show that the GM-PHD model achieved an average error of 0.1181 m, which is a 44.05% reduction compared to the LiDAR-based model. Meanwhile, the error of the proposed model can reach 0.501 m. When compared to the social LSTM model, the prediction error was reduced by 29.43% under the average displacement error (ADE) metric. The proposed method can provide data support and an effective theoretical basis for decision systems to improve traffic safety.

Development and Practice of Sports-Related Public Welfare Platform Based on Multi-Sensor Technology.

Today, more and more Internet public media platforms allowing people to make donations or seek help are being founded in China. However, there are few specialized sports-related public welfare platforms. In this paper, a sports-related public welfare platform that aims to help people who were disabled due to participation in sports and those who are disabled but want to participate in sports was developed based on multi-sensor technology. A multi-sensor data fusion algorithm was developed, and its estimation performance was verified by comparing it with the existing Kalman consistent filtering algorithm in terms of average estimation and average consistency errors. Experimental results prove that the speed of the data collection and analysis of the sports-related public welfare platform using the algorithm established in this paper was greatly improved. Relevant data on how users used this platform showed that various factors affected users' practical satisfaction with sports-related public welfare media platforms. It is suggested that a sports-related public welfare media platform should pay attention to the aid effect, and specific efforts should be devoted to improving the reliability and timeliness of public welfare aid information, and ensuring the stability of the platform system.

A New Evidence Weight Combination and Probability Allocation Method in Multi-Sensor Data Fusion.

A single sensor is prone to decline recognition accuracy in the face of a complex environment, while the existing multi-sensor evidence theory fusion methods do not comprehensively consider the impact of evidence conflict and fuzziness. In this paper, a new evidence weight combination and probability allocation method is proposed, which calculated the degree of evidence fuzziness through the maximum entropy principle, and also considered the impact of evidence conflict on fusing results. The two impact factors were combined to calculate the trusted discount and reallocate the probability function. Finally, Dempster's combination rule was used to fuse every piece of evidence. On this basis, experiments were first conducted to prove that the existing weight combination methods produce results contrary to common sense when handling high-conflicting and high-clarity evidence, and then comparative experiments were conducted to prove the effectiveness of the proposed evidence weight combination and probability allocation method. Moreover, it was verified, on the PAMAP2 data set, that the proposed method can obtain higher fusing accuracy and more reliable fusing results in all kinds of behavior recognition. Compared with the traditional methods and the existing improved methods, the weight allocation method proposed in this paper dynamically adjusts the weight of fuzziness and conflict in the fusing process and improves the fusing accuracy by about 3.3% and 1.7% respectively which solved the limitations of the existing weight combination methods.

Group Decision Making-Based Fusion for Human Activity Recognition in Body Sensor Networks.

Ensemble learning systems (ELS) have been widely utilized for human activity recognition (HAR) with multiple homogeneous or heterogeneous sensors. However, traditional ensemble approaches for HAR cannot always work well due to insufficient accuracy and diversity of base classifiers, the absence of ensemble pruning, as well as the inefficiency of the fusion strategy. To overcome these problems, this paper proposes a novel selective ensemble approach with group decision-making (GDM) for decision-level fusion in HAR. As a result, the fusion process in the ELS is transformed into an abstract process that includes individual experts (base classifiers) making decisions with the GDM fusion strategy. Firstly, a set of diverse local base classifiers are constructed through the corresponding mechanism of the base classifier and the sensor. Secondly, the pruning methods and the number of selected base classifiers for the fusion phase are determined by considering the diversity among base classifiers and the accuracy of candidate classifiers. Two ensemble pruning methods are utilized: mixed diversity measure and complementarity measure. Thirdly, component decision information from the selected base classifiers is combined by using the GDM fusion strategy and the recognition results of the HAR approach can be obtained. Experimental results on two public activity recognition datasets (The OPPORTUNITY dataset; Daily and Sports Activity Dataset (DSAD)) suggest that the proposed GDM-based approach outperforms the well-known fusion techniques and other state-of-the-art approaches in the literature.