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.

翻译：本文提出了一种针对空间点云数据的连续概率建模方法，采用有限高斯混合模型（GMM），其中模型分量数量根据场景复杂度自适应调整。现有层级化与自适应方法在平衡模型保真度与规模方面存在局限，而当前主流的建图方法需要针对特定应用场景调整参数，难以在不同环境中泛化。为弥补这一不足，本文利用信息论学习中的自组织原理，根据传感器数据中的相关信息自动调整高斯混合模型的复杂度。通过在实际场景复杂度各异的数据上，将本方法与现有多种点云建模技术进行对比评估，验证了其有效性。