


A Random Set Approach to SLAM
Authors: John Mullane, BaNu Vo, Martin Adams, Wijerupage Sarha Wijesoma.
Time: 11:1011:35
This presentation offers an alternative formulation for the Bayesian featurebased simultaneous localisation and mapping (SLAM) problem, using a random finite set approach. For a feature based map, SLAM requires the joint estimate of the vehicle location and the map. In most feature based SLAM algorithms, socalled “feature management” algorithms as well as data association hypotheses and extended Kalman filters are used to generate the joint posterior estimate. Current vectorvalued formulations require the data association problem, and map management issues, to be solved prior to the Bayesian state update. This is because the map estimates and measurements are rigidly ordered in a finitevectorvalued map state.
This presentation, however, shows a recursive filtering algorithm which jointly propagates both the estimate of the number of landmarks, their corresponding states, and the vehicle pose state, without the need for explicit feature management and data association algorithms. This is possible since the map estimates and measurements are represented by finitevaluedsets, in which no distinct order is assumed.
Using a random finitesetvalued joint vehiclemap state and setvalued measurements, the first order statistic of the set, called the intensity, is propagated through a probability hypothesis density (PHD) filter, from which estimates of the map and vehicle can be jointly extracted. An extendedKalman Gaussian Mixture implementation of the recursion is then tested for both featurebased robotic mapping (known location) and SLAM. Results from the experiments show improved performance for the proposed SLAM framework in environments of high spurious measurements.
