Discrete Fourier Transform-based Point Cloud Compression for Efficient SLAM in Featureless Terrain

TL;DR

This paper introduces a DFT-based point cloud compression method tailored for SLAM in featureless terrains, effectively reducing data size while maintaining accuracy for gradual landscapes like deserts and planets.

Contribution

It presents a novel DFT-based compression technique that omits high-frequency components, optimized for gradual terrains, improving SLAM efficiency in resource-limited environments.

Findings

Significant reduction in data size with minimal accuracy loss.

Effective for terrains with gradual elevation changes.

Validated on camera sequences of different terrains.

Abstract

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.