Recursive importance sampling for efficient grid-based occupancy filtering in dynamic environments

摘要

Bayesian Occupancy Filtering is an alternative to classical object tracking. Instead of estimating the state of objects in the environment, the latter is separated into equidistant cells. Tracking the occupancy state of these grid-cells is sufficient for many applications in robotics and cell-measurements can be easily produced from almost any kind of sensor. In [6] a sophisticated occupancy filter named BOFUM (Bayesian Occupancy Tracking using prior Map Knowledge) is introduced, which is able to infer velocities solely from occupancy measurements. It also features an advanced process model with motion uncertainty, which can be specialized for different application needs. In this paper we present an approach for recursively applying importance sampling (IS) to approximate the BOFUM calculations. The approach is similar to well known particle filters, but for a discrete cell perspective. In our experiments we achieved a speedup of at least 40-times by using the IS, thus making the algorithm applicable in real-world applications. We evaluate the consequences of approximation in an urban traffic scenario and also show the drawbacks of sampling.