基于离散傅里叶变换的点云压缩用于特征贫乏地形中的高效SLAM
Discrete Fourier Transform-based Point Cloud Compression for Efficient SLAM in Featureless Terrain
AI总结：
本文提出基于离散傅里叶变换的点云压缩方法，用于高效SLAM在特征贫乏地形中的应用，通过省略高频成分实现数据压缩，同时保持点云质量。
摘要：
同时定位与建图（SLAM）是无人机器人探索任务效率和可靠性的关键技术。尽管 onboard 计算能力和通信带宽受到极大限制，SLAM 处理的点云数据体积庞大，引发了数据压缩方法的关注。为解决这一问题，本文提出了一种利用离散傅里叶变换（DFT）压缩点云地图的新方法。所提出的技术将数字高程模型（DEM）转换为频率域的2D图像，并省略其高频成分，专注于行星和沙漠等渐进地形的探索。与具有详细结构的人工环境不同，高频成分对渐进地形的表示贡献很小。因此，该方法在不显著降低点云质量的情况下有效压缩了数据大小。我们通过两个具有不同高程剖面的地形的摄像序列，在压缩率和准确性方面评估了该方法。
英文摘要：
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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