用傅里叶变换压缩点云，提升无特征地形下的SLAM效率
Discrete Fourier Transform-based Point Cloud Compression for Efficient SLAM in Featureless Terrain
- 将数字高程图转至频域，舍弃高频分量实现压缩
- 在两种地形上压缩率提升显著，精度损失可控
- 适合行星、沙漠等平坦地形的机器人导航应用
同步定位与地图构建（SLAM）是无人机器人探索任务中确保效率和可靠性的重要技术。由于机载计算能力与通信带宽受限，而SLAM处理的点云数据量巨大，数据压缩方法受到关注。本文提出一种基于离散傅里叶变换（DFT）的点云地图压缩方法。该方法将数字高程模型（DEM）转换为频域二维图像，去除其高频分量，重点针对行星、沙漠等渐变地形的探索。相较于具有详细结构的人工环境，渐变地形中高频分量对表征贡献较小。因此该方法可在不显著降低点云质量的前提下有效压缩数据量。我们在两种具有不同高程特征的地形相机序列上评估了该方法的压缩率与精度。
原文摘要 · Abstract (English)
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.
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