The “fast clustering-tracking” algorithm in the Bayesian occupancy filter framework

It has been shown that the dynamic environment around the mobile robot can be efficiently and robustly represented by the Bayesian occupancy filter (BOF) (Tay, 2008). In the BOF framework, the environment is decomposed into a grid-based representation in which both the occupancy and the velocity distributions are estimated. In such a representation, concepts such as objects or tracks do not exist. However, the object-level representation is necessary for applications needing high-level representations of obstacles and their motion. To achieve this, we present in this paper a novel algorithm which performs clustering on the BOF output grid. The main idea is to use the prediction result of the tracking module as a form of feedback to the clustering module, which reduces drastically the complexity of the data association. Compared with the traditional joint probabilistic data association filter (JPDAF) approach, the proposed algorithm demands less computational costs, so as to be suitable for environments with large amount of dynamic objects. The experiment result on the real data shows the effectiveness of the algorithm.