In pedestrian navigation it is possible to learn the map of the environment simultaneously with a simultaneous localization and mapping (SLAM) algorithm. At DLR-KN, a SLAM algorithm especially designed for pedestrian navigation solely based on inertial measurements is developed which is called FootSLAM. This algorithm is further extended for collaboratively learning the map of the environment (FeetSLAM). The objective of collaborative mapping is that data from many walks can be combined to generate a more accurate and more encompassing total map. We have implemented an iterative processing algorithm motivated by Turbo Decoding from channel coding theory that takes maps from one data set as prior maps for other data sets. With a collaboratively learned prior map the position accuracy can be enhanced to be below 0.5 meters.
>> More informations about FootSLAM can be found here.
>> Publications List of Collaborative Mapping