# Multi-Sensor Data Fusion Method Based on Self-Attention Mechanism
## 1 Introduction
[10] proposed an adaptive fusion method for multi-source data that effectively and adaptively fuses multiple data sources and extracts valuable information based on the size of the convolution kernel. [PERSON] et al. [11] proposed an end-to-end deep neural network for the point-by-point fusion of LiDAR point clouds and aerial imagery, thereby improving the accuracy of three-dimensional (3D) segmentation and merging multi-view 3D scan data. [PERSON] et al. [12] proposed a multi-modal fusion approach for 3D robot trajectory estimation that combines two-dimensional (2D) features extracted from color images with 3D features extracted from point clouds. To achieve the more effective fusion of RGB images and point cloud features, [PERSON] et al. [13] proposed a multi-layer fusion model that iteratively combines features from multiple convolutional layers and effectively fuses global and local features. [PERSON] et al. [14] proposed a road detection method based on deep learning that achieved outstanding performance by fusing a laser point cloud and camera images using the proposed cross-fusion strategy. [PERSON] et al. [15] presented a novel 3D object detection algorithm based on cameras and LiDAR, which integrated point cloud features and image feature sampling. Further, they introduced a multi-head attention mechanism for feature fusion. [PERSON] et al. [16] utilized the data information of different sensors and the channel attention mechanism to enable multi-modal data to obtain reasonable fusion weights in the fusion of feature channels to better utilize the correlation and complementarity of different modal data. [PERSON] et al. [17] proposed a visual sensing and perception strategy based on LIDAR and camera fusion for the accurate localization of robots in real orchard environments. [PERSON] et al. [18] proposed a multi-sensor data fusion method that combines LIDAR and image data to achieve precise geometric alignment between LIDAR and image pixels. The fusion process employs a cross-attention mechanism to dynamically capture the correlation between images and LiDAR featuresto solve the 3D detection problem in autonomous driving. To achieve their objectives, the various multi-modal data fusion methods mentioned above not only explore data fusion models, but also integrate and modify existing models based on the characteristics and requirements of their own data fusion. In 3D reconstruction tasks, data from a single sensor or data source often exhibit limitations in terms of accuracy and precision. To overcome these limitations, this paper proposes a multi-sensor data fusion method based on a self-attention mechanism. Therefore, to overcome the limitations of accuracy and precision in single-sensor or single-data-source data, this paper proposes a multi-sensor data fusion method based on a self-attention mechanism. The multi-sensor data fusion model constructed in this study was used to obtain multi-source and multi-modal data. The obtained data were treated as inputs to different-dimensional convolutional neural networks for feature extraction. Subsequently, all the modal features were concatenated using fully connected layers, and a self-attention mechanism was employed to adjust the importance of each input feature, resulting in a fused feature representation. Finally, a multi-layer perceptron (MLP) was utilized to generate the final output. The primary objective of this method is to enhance the accuracy and precision of the final reconstruction results by effectively integrating information from different sensors or data sources during the 3D reconstruction process. This method effectively captures and maximally utilizes information from multiple sensors or data sources, thereby improving the accuracy and robustness of the fusion results. The remainder of this paper is organized as follows: Section 2 introduces the constructed multi-sensor data fusion model, including the sensor system used and its workflow.