SLAM based Selective Submap Joining for the Victoria Park Dataset

One of the main drawbacks of current SLAM algorithms is that they do not result in consistent maps of large areas, mainly because the uncertainties increase with the scenario. In addition, as the map size grows the computational costs increase, making SLAM solutions unsuitable for on-line applications. The use of local maps has been demonstrated to be useful in these situations, reducing computational cost and improving map consistency. Following this idea, this paper proposes a technique based on using independent local maps together with a global stochastic map. The global level contains the relative transformations between local maps, which are updated once a new loop is detected. In addition, the information within the local maps is also corrected. Thus, maps sharing a high number of features are updated through fusion and the correlation between landmarks and vehicle is maintained. Results on synthetic data and on the Victoria Park Dataset show that our approach is able to consistently map large areas and the computational costs are lower.