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一种基于摄像头和毫米波雷达的多模态信息融合算法
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Abstract:
环境感知是高级驾驶辅助系统(Advanced Driver Assistance Systems, ADAS)的关键环节，而摄像头和毫米波雷达是环境感知的核心传感器。利用多源传感器不同模态的互补信息，可以显著提升车辆的自主感知能力，帮助车辆更好地应对复杂场景下的目标检测任务。由于毫米波雷达点云的稀疏性，现有的摄像头和毫米波雷达融合算法，存在对毫米波雷达点云信息利用不充分、缺乏鲁棒性等特点。针对这些问题，本文提出了一种基于点云膨胀的数据级融合算法。算法首先使用提出的最近邻帧同步算法以及空间坐标映射进行多源数据时空对齐。之后，使用提出的基于点云膨胀的增强中心融合网络(Enhanced Center Fusion Net, ECFN)将映射至像素坐标系的毫米波雷达数据进行特征增强，并引入1 × 1的卷积核对输入数据进行降维，实现跨通道的信息交互。此外，ECFN还在损失函数中引入新的速度和深度因子来增强神经网络对雷达点云信息的利用。实验结果表明，增强的中心融合网络ECFN在推理时间稍有增加的情况下，平均精度优于基于单源传感器的算法以及现有的多源融合网络。
Environmental awareness is the key link of Advanced Driver Assistance Systems (ADAS), and camera and millimeter wave radar are the core sensors of environmental awareness. Using the complementary information of different modes of multi-source sensors can significantly improve the autonomous sensing ability of vehicles, and help vehicles better cope with the target detection task in complex scenes. Due to the sparsity of the millimeter wave radar point cloud, the existing camera and millimeter wave radar fusion algorithms do not make full use of the millimeter wave radar point cloud information and lack of robustness. Aiming at these problems, this paper proposes a data-level fusion algorithm based on point cloud expansion. The algorithm first uses the proposed nearest neighbor frame synchronization algorithm and spatial coordinate mapping to align mul-ti-source data in time and space. Then, the proposed Enhanced Center Fusion Net (ECFN) based on point cloud expansion is used to enhance the features of millimeter wave radar data mapped to the pixel coordinate system, and the 1 × 1 convolution kernel is introduced to reduce the dimension of the input data to realize cross-channel information interaction. In addition, ECFN also introduces new velocity and depth factors into the loss function to enhance the use of radar point cloud information by neural networks. The experimental results show that the average accuracy of the enhanced central fusion network ECFN is better than that of the algorithm based on a single source sensor and the existing multi-source fusion network when the inference time is slightly increased.
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