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：本信为空间点云数据提供了一个连续的概率模型方法,使用有限的高斯混合模型(GMMs)对空间点云数据进行连续的概率模型,根据场景复杂度对组成部分的数量进行调整,很少提出分级和适应性方法,以应对在模型忠度与大小之间保持平衡的挑战。相反,最先进的绘图方法需要具体使用案例的调试参数,但并不在不同的环境中进行。为了缩小这一差距,我们利用信息理论学习的自我组织原则,根据传感器数据中的有关信息,自动调整GMM模型的复杂性。该方法根据现实世界数据中不同程度复杂度的现有点云建模型技术进行评估。