A Three Dimensional Movement Model for Pedestrian Navigation

Pedestrian mobility models can be used for numerous applications such as infrastructure design, evacuation planning, architecture, robot-human interaction, pervasive computing or navigation and localization. Within the scope of this paper, the purpose of such models is to realistically represent the stochastic nature of three dimensional pedestrian’s movement. Our own application focus is to generate a “movement” or transition model for sequential Bayesian filtering techniques, such as particle-filtering [AMGC02] [GSS93], but the method can be applied to many of the above application domains. In this paper the two dimensional movement model presented in [KKRA08] is extended in order to be able to accommodate pedestrian movement in the rectangular coordinate system of X, Y and Z. Here, X and Y represent the earth surface plane in a local region, and Z is the upward normal vector to that plane. The result is a three dimensional mobility model that is capable of representing pedestrian movement in challenging indoor and outdoor localization environments such as multi-floor buildings. It actually consists of two constituent movement models, operating at the microscopic level and suitable for pedestrian navigation. The constituents are a Three Dimensional Stochastic Behavioral Movement Model (3D-SBMM) to characterize random motion and a Three Dimensional Diffusion Movement Model (3DDMM) to characterize geographical goals a pedestrian might walk towards. In order to account for the fact that humans might switch between more goal-directed motion and more random motion, a top-level Markov process is designed to determine whether to currently use the 3D-SBMM or the 3DDMM. Therefore, the model switches between motion that is more goal-oriented (3D-DMM) or stochastic. The designed model is implemented and tested in an already available distributed simulation and demonstration indoor/outdoor environment for mobility, location and context applications. Applications of pedestrians’ movement models, as well as pre-requisites for a localization movement model and summary of related work will be discussed in section 1. The three dimensional movement model, its constituents, properties and computations will be explained in details in section 2. System design and implementation will be illustrated in section 3. Simulation results will be given in section 4. Finally, some conclusions and future work will be given in section 5.