  With advances in image processing and machine learning, it is now feasible to
incorporate semantic information into the problem of simultaneous localisation
and mapping (SLAM). Previously, SLAM was carried out using lower level
geometric features (points, lines, and planes) which are often view-point
dependent and error prone in visually repetitive environments. Semantic
information can improve the ability to recognise previously visited locations,
as well as maintain sparser maps for long term SLAM applications. However, SLAM
in repetitive environments has the critical problem of assigning measurements
to the landmarks which generated them. In this paper, we use k-best assignment
enumeration to compute marginal assignment probabilities for each measurement
landmark pair, in real time. We present numerical studies on the KITTI dataset
to demonstrate the effectiveness and speed of the proposed framework.
