Robust localization is a prerequisite for mobile robot autonomy. In many situations the GPS signal is not available and thus an additional localization system is required. A simple approach is to apply localization based on dead reckoning by use of wheel encoders but it results in large estimation errors. With exteroceptive sensors such as a laser range finder natural landmarks in the environment of the robot can be extracted from raw range data. Landmarks are extracted with the Hough transform and a recursive line segment algorithm. By applying data association and Kalman filtering along with process models the landmarks can be used in combination with wheel encoders for estimating the global position of the robot. If several robots can cooperate better position estimates are to be expected because robots can be seen as mobile landmarks and one robot can supervise the movement of another. The centralized Kalman filter presented in this master thesis systematically treats robots and extracted landmarks such that benefits from several robots are utilized. Experiments in different indoor environments with two different robots show that long distances can be traveled while the positional uncertainty is kept low. The benefit from cooperating robots in the sense of reduced positional uncertainty is also shown in an experiment. Except for localization algorithms a typical autonomous robot task in the form of change detection is solved. The change detection method, which requires robust localization, is aimed to be used for surveillance. The implemented algorithm accounts for measurement- and positional uncertainty when determining whether something in the environment has changed. Consecutive true changes as well as sporadic false changes are detected in an illustrative experiment.

In this dissertation, we focus on two fundamental problems related to the navigation of ground robots and small Unmanned Aerial Vehicle (UAVs): cooperative localization and path planning. The theme running through in all of the work is the use of bearing only sensors, with a focus on monocular video cameras mounted on ground robots and UAVs. To begin with, we derive the conditions for the complete observability of the bearing-only cooperative localization problem. The key element of this analysis is the Relative Position Measurement Graph (RPMG). The nodes of an RPMG represent vehicle states and the edges represent bearing measurements between nodes. We show that graph theoretic properties like the connectivity and the existence of a path between two nodes can be used to explain the observability of the system. We obtain the maximum rank of the observability matrix without global information and derive conditions under which the maximum rank can be achieved. Furthermore, we show that for the complete observability, all of the nodes in the graph must have a path to at least two different landmarks of known location. The complete observability can also be obtained without landmarks if the RPMG is connected and at least one of the robots has a sensor which can measure its global pose, for example a GPS receiver. We validate these conditions by simulation and experimental results. The theoretical conditions to attain complete observability in a localization system is an important step towards reliable and efficient design of localization and path planning algorithms. With such conditions, a designer does not need to resort to exhaustive simulations and/or experimentation to verify whether a given selection of a control strategy, topology of the sensor network, and sensor measurements meets the observability requirements of the system. In turn, this leads to decreased requirements of time, cost, and effort for designing a localization algorithms. We use these observability conditions to develop a technique, for camera equipped UAVs, to cooperatively geo-localize a ground target in an urban terrain. We show that the bearing-only cooperative geo-localization technique overcomes the limitation of requiring a low-flying UAV to maintain line-of-sight while flying high enough to maintain GPS lock. We design a distributed path planning algorithm using receding horizon control that improves the localization accuracy of the target and of all of the UAVs while satisfying the observability conditions. Next, we use the observability analysis to explicitly design an active local path planning algorithm for UAVs. The algorithm minimizes the uncertainties in the time-to-collision (TTC) and bearing estimates while simultaneously avoiding obstacles. Using observability analysis we show that maximizing the observability and collision avoidance are complementary tasks. We provide sufficient conditions of the environment which maximizes the chances obstacle avoidance and UAV reaching the goal. Finally, we develop a reactive path planner for UAVs using sliding mode control such that it does not require range from the obstacle, and uses bearing to obstacle to avoid cylindrical obstacles and follow straight and curved walls. The reactive guidance strategy is fast, computationally inexpensive, and guarantees collision avoidance.