Experiments on augmenting CONDENSATION for mobile robot localization

We study some modifications of the CONDENSATION algorithm. The case studied is feature based mobile robot localization in a large scale environment. The required sample set size for making the CONDENSATION algorithm converge properly can in many cases require too much computation. To manage with a sample set size which in the normal case would cause the CONDENSATION algorithm to break down. We study two modifications. The first strategy, called "CONDENSATION with random sampling", takes part of the sample set and spreads it randomly over the environment the robot operates in. The second strategy, called "CONDENSATION with planned sampling", places part of the sample set at planned positions based on the detected features. From the experiments we conclude that the second strategy is the best and can reduce the sample set size by at feast a factor of 40.