A MMW Radar Indoor Mapping Method Based on Transfer Learning

Peiyan Tu, Tao He, Zhikai Yang, Zhanyu Zhu
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Abstract

A millimeter-wave radar indoor mapping method based on Transfer Learning to generate dense detection data as Lidar’s output is proposed in this paper. This method uses the NN model with CycleCAN architecture to learn the Lidar-like map pieces, to enhance the mmw radar mapping performance. With the ideal of Transfer Learning, the model is trained using simulated data generated by CARLA and deployed into physical system to improve the mmw radar mapping performance. Simulation and practice measurement experiments are carried out to prove the coefficients of this method, and the quantitative analysis is conducted to evaluate the mapping quality.
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一种基于迁移学习的毫米波雷达室内制图方法
提出了一种基于迁移学习的毫米波雷达室内测绘方法,生成密集的探测数据作为激光雷达的输出。该方法利用具有CycleCAN架构的神经网络模型学习类激光雷达地图块,提高毫米波雷达的成图性能。利用迁移学习的思想,利用CARLA生成的模拟数据对模型进行训练,并将其部署到物理系统中,以提高毫米波雷达制图性能。通过仿真和实测实验验证了该方法的各项系数,并进行了定量分析,对测绘质量进行了评价。
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