Simultaneous Localization and Mapping (SLAM) is an essential technology for the efficiency and reliability of unmanned robotic exploration missions. While the onboard computational capability and communication bandwidth are critically limited, the point cloud data handled by SLAM is large in size, attracting attention to data compression methods. To address such a problem, in this paper, we propose a new method for compressing point cloud maps by exploiting the Discrete Fourier Transform (DFT). The proposed technique converts the Digital Elevation Model (DEM) to the frequency-domain 2D image and omits its high-frequency components, focusing on the exploration of gradual terrains such as planets and deserts. Unlike terrains with detailed structures such as artificial environments, high-frequency components contribute little to the representation of gradual terrains. Thus, this method is effective in compressing data size without significant degradation of the point cloud. We evaluated the method in terms of compression rate and accuracy using camera sequences of two terrains with different elevation profiles.

翻译：同步定位与建图（SLAM）是提升无人机器人勘探任务效率与可靠性的关键技术。在机载计算能力与通信带宽严重受限的情况下，SLAM处理的点云数据规模庞大，使得数据压缩方法备受关注。针对该问题，本文提出一种利用离散傅里叶变换（DFT）压缩点云地图的新方法。该技术将数字高程模型（DEM）转换为频域二维图像并剔除其高频分量，主要面向行星、沙漠等渐变地形的勘探。与人工环境等具有精细结构的地形不同，高频分量对渐变地形的表征贡献甚微。因此，该方法能在不明显降低点云质量的前提下有效压缩数据规模。我们使用两种不同高程剖面地形的相机序列，在压缩率与精度方面对该方法进行了评估。