智能设备中传感器融合技术的优化研究
摘 要
随着智能设备的快速发展，传感器融合技术在提升设备感知能力方面发挥着关键作用。本研究针对现有传感器融合算法在复杂环境下精度不足、计算效率低等问题，提出了一种基于深度学习的自适应多源数据融合优化方法。研究首先构建了包含惯性测量单元、视觉传感器和激光雷达的多模态数据集，通过改进的卷积神经网络提取特征信息；其次设计了基于注意力机制的特征权重分配策略，实现了对异质传感器数据的动态加权融合；最后引入轻量化网络结构优化算法，显著降低了计算复杂度。本研究的创新点在于提出了可适应不同环境条件的自适应融合机制，并实现了算法的高效部署。研究成果为智能设备的精准感知提供了新的技术路径，在自动驾驶、智能机器人等领域具有重要的应用价值。
关键词：传感器融合 深度学习 多源数据融合
Abstract
With the rapid development of smart devices, sensor fusion technology plays a key role in improving device perception capabilities. This paper proposes an adaptive multi-source data fusion optimization method for the existing sensor fusion algorithm with insufficient accuracy and low computing efficiency in complex environment. Firstly, constructing a multimodal data set including inertial measurement unit, visual sensor and lidar, extracting feature information through improved convolutional neural network; second, designing a feature weight allocation strategy based on attention mechanism to realize the dynamic weighted fusion of heterogeneous sensor data; finally, introducing lightweight network structure optimization algorithm to significantly reduce the computational complexity. The innovation of this study lies in the adaptive fusion mechanism adapted to different environmental conditions and the efficient deployment of the algorithm. The research results provide a new technical path for the accurate perception of intelligent devices, and have important application value in the fields of autonomous driving and intelligent robots.
Keyword: Sensor fusion deep learning multi-source data fusion
目 录
1绪论 1
1.1研究背景 1
1.2研究现状分析 1
2智能设备传感器融合关键技术分析 1
2.1多源异构传感器数据采集优化 1
2.2传感器数据预处理方法改进 2
2.3基于深度学习的特征提取优化 3
3智能设备传感器融合算法优化研究 3
3.1卡尔曼滤波算法的改进策略 3
3.2粒子滤波算法的性能优化 4
3.3多模态数据融合算法设计 5
4智能设备传感器融合系统实现与验证 5
4.1系统架构设计与实现方案 5
4.2实验环境搭建与数据集构建 6
4.3系统性能评估与结果分析 6
5结论 7
参考文献 8
致谢 9
摘 要
随着智能设备的快速发展，传感器融合技术在提升设备感知能力方面发挥着关键作用。本研究针对现有传感器融合算法在复杂环境下精度不足、计算效率低等问题，提出了一种基于深度学习的自适应多源数据融合优化方法。研究首先构建了包含惯性测量单元、视觉传感器和激光雷达的多模态数据集，通过改进的卷积神经网络提取特征信息；其次设计了基于注意力机制的特征权重分配策略，实现了对异质传感器数据的动态加权融合；最后引入轻量化网络结构优化算法，显著降低了计算复杂度。本研究的创新点在于提出了可适应不同环境条件的自适应融合机制，并实现了算法的高效部署。研究成果为智能设备的精准感知提供了新的技术路径，在自动驾驶、智能机器人等领域具有重要的应用价值。
关键词：传感器融合 深度学习 多源数据融合
Abstract
With the rapid development of smart devices, sensor fusion technology plays a key role in improving device perception capabilities. This paper proposes an adaptive multi-source data fusion optimization method for the existing sensor fusion algorithm with insufficient accuracy and low computing efficiency in complex environment. Firstly, constructing a multimodal data set including inertial measurement unit, visual sensor and lidar, extracting feature information through improved convolutional neural network; second, designing a feature weight allocation strategy based on attention mechanism to realize the dynamic weighted fusion of heterogeneous sensor data; finally, introducing lightweight network structure optimization algorithm to significantly reduce the computational complexity. The innovation of this study lies in the adaptive fusion mechanism adapted to different environmental conditions and the efficient deployment of the algorithm. The research results provide a new technical path for the accurate perception of intelligent devices, and have important application value in the fields of autonomous driving and intelligent robots.
Keyword: Sensor fusion deep learning multi-source data fusion
目 录
1绪论 1
1.1研究背景 1
1.2研究现状分析 1
2智能设备传感器融合关键技术分析 1
2.1多源异构传感器数据采集优化 1
2.2传感器数据预处理方法改进 2
2.3基于深度学习的特征提取优化 3
3智能设备传感器融合算法优化研究 3
3.1卡尔曼滤波算法的改进策略 3
3.2粒子滤波算法的性能优化 4
3.3多模态数据融合算法设计 5
4智能设备传感器融合系统实现与验证 5
4.1系统架构设计与实现方案 5
4.2实验环境搭建与数据集构建 6
4.3系统性能评估与结果分析 6
5结论 7
参考文献 8
致谢 9
