Probabilistic Rule Set Joint State Update as approximation to the full joint state estimation applied to multi object scene analysis

One essential capability of service robots lies in the identification and localization of objects in the vicinity of the robot. The extreme computational demands of this high-dimensional state estimation problem require approximations of the joint posterior even for small numbers of objects. A common approach to solve this problem is to marginalize the joint state space and to consider object-related state spaces which are estimated individually under the assumption of statistical independence. In practice, however, this independence assumption is often violated, especially when the objects are located close to each other, which leads to a reduced accuracy of this approximation, compared to the full joint estimation. To address this problem, we propose the new method denoted as Rule Set Joint State Update (RSJSU), which features a better approximation of the joint posterior in the presence of dependencies, and thus leads to better estimation results. We present experimental results in which we simultaneously estimate all six degrees of freedom of multiple objects.