An Object-Tracking Algorithm for 3-D Range Data Using Motion and Surface Estimation

Given a series of point sets sampled from a rigid surface by a 3-D rangefinder, we study the problem of estimating the motion and surface structure of a dynamic object. This target tracking problem with 3-D data can be formulated as maximizing the likelihood of the data (the scan map) and the Gaussian mixture model (GMM; object model up to the previous time step). We choose the prior for the object model from the conjugate distribution family of the GMM to yield a trackable posterior distribution for the object model. This GMM-based nonparametric model can be indexed by a hash lookup table, and we show that the method´s complexity linearly scales with the number of scan points. Quantitative performance evaluation demonstrates that the proposed method substantially outperforms others. Results of road tests in divided freeway and urban scenes show the accuracy and robustness of the system, which can enable many vehicle active-safety and driver-assistance applications.