Rainer K├╝mmerle.
State Estimation and Optimization for Mobile Robot Navigation.
PhD Thesis, Albert-Ludwigs-University of Freiburg, Department of Computer Science, April 2013.


Robust autonomous navigation is a key feature of a mobile robot realizing services such as transportation, cleaning, search and rescue, and surveillance. In addition to that, navigation is a building block for a robot assisting humans in potentially dangerous situations, such as search-and-rescue scenarios. Hence, navigation is one of the major research topics in the robotics community. To realize the above mentioned applications, we need to fulfill certain requirements, so that a robot is regarded as useful. For example, a robot which performs pick-and-place tasks or offers guidance in city centers needs to be aware of its own position in the environment and it needs to have an accurate model of the environment for planning an appropriate path. A robot which should guide a human to a certain place or has to deliver goods is only regarded as helping hand, if the location is reliably reached within the expected time frame. Particularly, estimating the state which describes the current situation of the navigation system is complex. In this thesis, we focus on efficient and accurate state estimation techniques which apply probabilistic algorithms. An example for such a state estimation task is the Simultaneous Localization and Mapping (SLAM) problem, in which a robot has to address both aspects. First, it needs to estimate what the environment looks like. This is the mapping part which deals with integrating the information obtained by the sensors of the robot into an appropriate representation. Second, the localization component has to estimate the position of the robot with respect to the model of the environment. In the first part of this thesis, we present efficient approaches to estimate the state of the robot while performing SLAM. Our approach allows a robot to accurately estimate the model of the environment in an online setting and also in situations when provided with a poor initial guess. Additionally, we provide an empirical evaluation which demonstrates the advantages of our approach compared to other state-of-the-art methods. Subsequently, we extend our state estimation approach to also include the unknown calibration parameters, which might change during the lifetime of the robot, to incorporate prior information about the structure of the environment, and to improve the fine-grained details of the estimated models. In the second part of this thesis, we demonstrate two challenging applications which we realized by building upon and extending the algorithms presented in the first part. In detail, we discuss an approach which allows a car to autonomously park in a complex multi-level parking garage. As second application we present a robotic pedestrian assistant which is able to navigate in densely populated pedestrian zones. All techniques presented in this thesis have been implemented and tested using both real-world data collected with mobile robots and simulated data. To support our claims, we performed an extensive collection of experiments, in which we compared the performance of our approaches with the state-of-the-art. We believe that the proposed approaches will allow us in the future to build systems that can assist humans in their homes and at their workplaces.

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BibTeX entry:

  author = {K{\"u}mmerle, Rainer},
  school = {Albert-Ludwigs-University of Freiburg, Department of Computer Science},
  title = {State Estimation and Optimization for Mobile Robot Navigation},
  month = {April},
  year = {2013}