Multi-Band Bayesian Particle Filter for Occupancy Grid Mapping

In the realm of advanced driver assistance systems and autonomous vehicles, the ability to accurately perceive and interpret the vehicle's environment is not just a requirement - it's the cornerstone upon which all functionality is built. One prevalent method of representing the environment is through the use of an occupancy grid map. This map segments the environment into distinct grid cells, each of which is evaluated to determine if it is occupied or free. This evaluation operates under the assumption that each grid cell is independent of the others. The underlying mathematical structure of this system is the binary Bayes filter (BBF). The BBF integrates sensor data from various sources and can incorporate measurements taken at different times. The occupancy grid map does not rely on the identification of individual objects, which allows it to depict obstacles of any shape. This flexibility is a key advantage of this approach. This study introduces a novel approach to dynamic grid mapping, conceptualized as an approximation of a Random Finite Set (RFS) filter. An RFS is a probabilistic representation of a finite, random collection of objects and their respective states. Finite Set Statistics (FISST) provide a framework for Bayesian filtering of these random finite sets and form the foundation for several multi-object tracking methodologies, such as the Probability Hypothesis Density (PHD) filter. By characterizing the grid as an RFS, we can apply sophisticated concepts from the well-established domain of RFS filtering to dynamic grid mapping. The research develops a filter known as the Probability Hypothesis Density/Multi-Instance Bernoulli (PHD/MIB) filter. This filter alternately represents and propagates the dynamic grid map as a PHD and as multiple instances of Bernoulli filters, thereby offering a more integrated and efficient approach to dynamic environment estimation. In conclusion, the research highlights the DS-PHD/MIB filter's attributes, comparing its strengths and weaknesses with object-based tracking methods. Validation using the A2D2 Audi dataset demonstrates the filter's consistent state estimation and robust handling of multi-object scenarios in real-world driving conditions. Moreover, the evaluation affirms the real-time capability of the parallelized implementation of the DS-PHD/MIB filter. It validates its utility for state estimation in dynamic vehicle environments, thereby underscoring its potential for practical applications in dynamic environment estimation. This comprehensive approach offers a promising avenue for future research and development in this field.