Handling of Multiple Measurement Hypotheses in an Efficient Labeled Multi-Bernoulli Filter
Dominik Kellner, Michael Aeberhard · 2018
The detection and motion estimation of an unknown number of traffic participants in dense, cluttered environments is an essential task for autonomous driving systems. Recent research using Random Finite Sets show promising results and are the state-of-the-art for object tracking. This paper proposes an extension to the Labeled Multi-Bernoulli Filter (LMB) for handling multiple measurement hypotheses as they can occur with object detection using lidar, camera and radar. Real-time performance is achieved using an efficient Gibbs sampling, which directly handles multiple measurement hypotheses. The algorithm and its modifications are analyzed in detail using a simple example. Finally, two simulations show that the proposed algorithm is able to handle multiple measurement hypotheses better than the standard LMB filter. The performance increases even if these hypotheses have a significant systematic, non-Gaussian error.