Probabilistic Point Cloud Modeling via Self-Organizing Gaussian Mixture Models

Kshitij Goel, Nathan Michael, and Wennie Tabib

IEEE Robotics and Automation Letters (RA-L), Vol. 8 Issue 5, May 2023

teaser
The methodology proposed in this work enables multi-modal reconstruction at varying scales. Without modifying parameters, the methodology models depth and grayscale data of small objects (1 m x 1 m safety cone in the left image) and complex environments (10 m x 5 m cave in the center image) while also modeling depth and thermal data of (right) large-scale buildings (42 m x 28 m).

Abstract

This letter presents a continuous probabilistic modeling methodology for spatial point cloud data using finite Gaussian Mixture Models (GMMs) where the number of components are adapted based on the scene complexity. Few hierarchical and adaptive methods have been proposed to address the challenge of balancing model fidelity with size. Instead, state-of-the-art mapping approaches require tuning parameters for specific use cases, but do not generalize across diverse environments. To address this gap, we utilize a self-organizing principle from information-theoretic learning to automatically adapt the complexity of the GMM model based on the relevant information in the sensor data. The approach is evaluated against existing point cloud modeling techniques on real-world data with varying degrees of scene complexity.

Idea

figure_description
(Left) An illustration of the key idea behind the proposed methodology. Given a registered pair of intensity and depth images, the principle of relevant information (PRI) determines the number of components required to model the 4D point cloud associated with the images using a GMM. (Right) The effect of using the PRI is automatic adjustment in model complexity across scenes of different fidelity.

Video

Presentation

Citation

@article{goel2023probabilistic,
	title={Probabilistic point cloud modeling via self-organizing Gaussian mixture models},
	author={Goel, Kshitij and Michael, Nathan and Tabib, Wennie},
	journal={IEEE Robotics and Automation Letters},
	volume={8},
	number={5},
	pages={2526--2533},
	year={2023},
	publisher={IEEE}
}

Acknowledgments

We gratefully acknowledge Shivam Vats for helpful discussions regarding the mean shift algorithm.