ERCM: Bionic Event-based Registration Method Based on Contrast Minimum for Intelligent Unmanned Systems

Shijie Zhang, Fan Sang, Jiaxin Li, T. Tang, Jianglong Zhang, Taogang Hou
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Abstract

Event cameras have the advantages of high time resolution and high dynamic range, and the fusion of its image and RGB image can bring great benefits to intelligent unmanned systems. However, the two modal images are not fully registered, which may affect the performance of tasks in intelligent systems, such as semantic segmentation. In this paper, we propose a registration method of event and RGB cameras based on contrast minimum. Our experimental data consists of 8 sequences, each consisting of 20 consecutive frames from the DSEC dataset, an event-RGB image dataset. As a result, the overlaid image of event images and RGB images show better consistency in visualization. Compared with the baseline, the declining value of contrast is from 26 to 161, the proportion of overlapping pixels in edge and event image has increased by 2.00% and 0.91%, respectively. The study in this paper demonstrates promising applications of the fusion of event and RGB cameras in intelligent unmanned systems.
基于对比度最小的智能无人系统仿生事件配准方法
事件摄像机具有高时间分辨率和高动态范围的优点,其图像与RGB图像的融合可以为智能无人系统带来巨大的好处。然而,这两个模态图像没有完全配准,这可能会影响智能系统中任务的性能,例如语义分割。本文提出了一种基于对比度最小值的事件相机和RGB相机的配准方法。实验数据由8个序列组成,每个序列由来自事件- rgb图像数据集DSEC数据集的20个连续帧组成。结果表明,事件图像的叠加图像与RGB图像在可视化上具有较好的一致性。与基线相比,对比度下降值从26到161,边缘和事件图像中重叠像素的比例分别提高了2.00%和0.91%。本文的研究展示了事件与RGB相机融合在智能无人系统中的应用前景。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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