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Using probabilistic reasoning to model and perceive driving environments is a challenging problem due to both geometric and dynamic random natures. The occupancy grid system, providing an intermediate representation for complicated environments and having many beneficial implications (such as avoiding direct data association and more freedom in data fusion), has been increasingly becoming a popular...
Occupancy grid mapping is a well-known environment perception approach. A grid map divides the environment into cells and estimates the occupancy probability of each cell based on sensor measurements. An important extension is the Bayesian occupancy filter (BOF), which additionally estimates the dynamic state of grid cells and allows modeling changing environments. In recent years, the BOF attracted...
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