Extended target tracking using PHD filters

The world in which we live is becoming more and more automated, exemplified by the numerous robots, or autonomous vehicles, that operate in air, on land, or in water. These robots perform a wide array of different tasks, ranging from the dangerous, such as underground mining, to the boring, such as vacuum cleaning. In common for all different robots is that they must possess a certain degree of awareness, both of themselves and of the world in which they operate. This thesis considers aspects of two research problems associated with this, more specifically the Simultaneous Localization and Mapping (slam) problem and the Multiple Target Tracking (mtt) problem. The slam problem consists of having the robot create a map of an environment and simultaneously localize itself in the same map. One way to reduce the effect of small errors that inevitably accumulate over time, and could significantly distort the SLAM result, is to detect loop closure. In this thesis loop closure detection is considered for robots equipped with laser range sensors. Machine learning is used to construct a loop closure detection classifier, and experiments show that the classifier compares well to related work. The resulting slam map should only contain stationary objects, however the world also contains moving objects, and to function well a robot should be able to handle both types of objects. The mtt problem consists of having the robot keep track of where the moving objects, called targets, are located, and how these targets are moving. This function has a wide range of applications, including tracking of pedestrians, bicycles and cars in urban environments. Solving the mtt problem can be decomposed into two parts: one part is finding out the number of targets, the other part is finding out what the states of the individual targets are. In this thesis the emphasis is on tracking of so called extended targets. An extended target is a target that can generate any number of measurements, as opposed to a point target that generates at most one measurement. More than one measurement per target raise interesting possibilities to estimate the size and the shape of the target. One way to model the number of targets and the target states is to use random finite sets, which leads to the Probability Hypothesis Density (phd) filters. Two implementations of an extended target phd filter are given, one using Gaussian mixtures and one using Gaussian inverse Wishart (giw) mixtures. Two models for the size and shape of an extended target measured with laser range sensors are suggested. A framework for estimation of the number of measurements generated by the targets is presented, and reduction of giw mixtures is addressed. Prediction, spawning and combination of extended targets modeled using giw distributions is also presented. The extended target tracking functions are evaluated in simulations and in experiments with laser range data.