  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.
