Advances in Probabilistic Modeling: Applications of Stochastic Geometry [From the Guest Editors]
Martin David Adams, Ba‐Ngu Vo, Ronald Mahler · IEEE Robotics & Automation Magazine · 2014
The articles in this special section advocate that the same principle applies to feature detection and autonomous mapping in robotics, where, instead of referring to the problem of target estimation, the problem of map feature or environmental object estimation are of concern. From here on, map features, targets, and environmental objects of interest will simply be referred to as “features.” In the case of robotic mapping and SLAM, realistic feature detection algorithms produce false alarms and missed detections, and estimating the true number of map features is, therefore, central to these problems.