Compact and information loss-bounded estimation of Gaussian mixture model for 3D spatial representation

A new Gaussian mixture model (GMM) estimation technique is presented for three-dimensional (3D) spatial representation. The GMM generated by the proposed technique is compact with bounded information loss as a result of using robust estimators and the Kullback-Leibler divergence-based Gaussian mixture reduction method. In addition, the proposed technique is not only robust to outliers, but quite close to invariant under similarity transformation. Experiments have demonstrate that the compactness and the consistency of the GMM are improved compared with existing 3D spatial representation models.