Exploring the Frequency Domain Point Cloud Processing for Localisation Purposes in Arboreal Environments

• Published in: Algorithms Vol. 18; no. 8; p. 522
• Main Authors: Devanna, Rosa Pia, Torres-Torriti, Miguel, Sacilik, Kamil, Cetin, Necati, Auat Cheein, Fernando
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 18.08.2025
• Subjects: Accuracy; Deep learning; Eigenvalues; Eigenvectors; Forests; Fourier transforms; Frequencies; Frequency domain analysis; Graph representations; Hypotheses; Image registration; Localization; Mapping; Principal components analysis; Registration; Semantics; Sensors; Signal processing; Three dimensional models
• ISSN: 1999-4893; 1999-4893
• DOI: 10.3390/a18080522

Point clouds from 3D sensors such as LiDAR are increasingly used in agriculture for tasks like crop characterisation, pest detection, and leaf area estimation. While traditional point cloud processing typically occurs in Cartesian space using methods such as principal component analysis (PCA), this paper introduces a novel frequency-domain approach for point cloud registration. The central idea is that point clouds can be transformed and analysed in the spectral domain, where key frequency components capture the most informative spatial structures. By selecting and registering only the dominant frequencies, our method achieves significant reductions in localisation error and computational complexity. We validate this approach using public datasets and compare it with standard Iterative Closest Point (ICP) techniques. Our method, which applies ICP only to points in selected frequency bands, reduces localisation error from 4.37 m to 1.22 m (MSE), an improvement of approximately 72%. These findings highlight the potential of frequency-domain analysis as a powerful and efficient tool for point cloud registration in agricultural and other GNSS-challenged environments.