Topics in Localization and Mapping

Abstract: The need to determine ones position is common and emerges in many different situations. Tracking soldiers or a robot moving in a building or aiding a tourist exploring a new city, all share the questions ”where is the unit?“ and ”where is the unit going?“. This is known as the localization problem.Particularly, the problem of determining ones position in a map while building the map at the same time, commonly known as the simultaneous localization and mapping problem (slam), has been widely studied. It has been performed in cities using different land bound vehicles, in rural environments using au- tonomous aerial vehicles and underwater for coral reef exploration. In this thesis it is studied how radar signals can be used to both position a naval surface ves- sel but also to simultaneously construct a map of the surrounding archipelago. The experimental data used was collected using a high speed naval patrol boat and covers roughly 32 km. A very accurate map was created using nothing but consecutive radar images.A second contribution covers an entirely different problem but it has a solution that is very similar to the first one. Underwater sensors sensitive to magnetic field disturbances can be used to track ships. In this thesis, the sensor positions them- selves are considered unknown and are estimated by tracking a friendly surface vessel with a known magnetic signature. Since each sensor can track the vessel, the sensor positions can be determined by relating them to the vessel trajectory. Simulations show that if the vessel is equipped with a global navigation satellite system, the sensor positions can be determined accurately.There is a desire to localize firefighters while they are searching through a burn- ing building. Knowing where they are would make their work more efficient and significantly safer. In this thesis a positioning system based on foot mounted in- ertial measurement units has been studied. When such a sensor is foot mounted, the available information increases dramatically since the foot stances can be de- tected and incorporated in the position estimate. The focus in this work has therefore been on the problem of stand still detection and a probabilistic frame- work for this has been developed. This system has been extensively investigated to determine its applicability during different movements and boot types. All in all, the stand still detection system works well but problems emerge when a very rigid boot is used or when the subject is crawling. The stand still detection frame- work was then included in a positioning framework that uses the detected stand stills to introduce zero velocity updates. The system was evaluated using local- ization experiments for which there was very accurate ground truth. It showed that the system provides good position estimates but that the estimated heading can be wrong, especially after quick sharp turns.