A Particle-Based Solution for Modeling and Tracking Dynamic Digital Elevation Maps

Digital elevation maps are simple yet powerful representations of complex 3-D environments. These maps can be built and updated using various sensors and sensorial data processing algorithms. This paper describes a novel approach for modeling the dynamic 3-D driving environment, the particle-based dynamic elevation map, each cell in this map having, in addition to height, a probability distribution of speed in order to correctly describe moving obstacles. The dynamic elevation map is represented by a population of particles, each particle having a position, a height, and a speed. Particles move from one cell to another based on their speed vectors, and they are created, multiplied, or destroyed using an importance resampling mechanism. The importance resampling mechanism is driven by the measurement data provided by a stereovision sensor. The proposed model is highly descriptive for the driving environment, as it can easily provide an estimation of the height, speed, and occupancy of each cell in the grid. The system was proven robust and accurate in real driving scenarios, by comparison with ground truth data.