Adaptive and efficient mixture-based representation for range data

Modern range sensors generate millions of data points per second, making it difficult to utilize all incoming data effectively in real time for devices with limited computational resources. The Gaussian mixture model (GMM) is a convenient and essential tool commonly used in many research domains. In this paper, an environment representation approach based on the hierarchical GMM structure is proposed, which can be utilized to model environments with weighted Gaussians. The hierarchical structure accelerates training by recursively segmenting local environments into smaller clusters. By adopting the information-theoretic distance and shape of probabilistic distributions, weighted Gaussians can be dynamically allocated to local environments in an arbitrary scale, leading to a full adaptivity in the number of Gaussians. Evaluations are carried out in terms of time efficiency, reconstruction, and fidelity using datasets collected from different sensors. The results demonstrate that the proposed approach is superior with respect to time efficiency while maintaining the high fidelity as compared to other state-of-the-art approaches.