Robust Graph-Based Localization and Mapping

Localization and mapping are fundamental requirements to enable mobile robots to operate robustly in their environments. The solution of these two problems will allow robots to assist humans in important tasks such as autonomous driving or hazardous search-and-rescue missions. These complex navigation tasks require a robot to build a map while simultaneously localizing itself in it. It is relatively straightforward to build a map with perfect sensors. The challenge arises when a mobile robot needs to perform both mapping and localization with noisy sensors. This requires a robot to reliably recognize a previously visited place, while being robust to noise in sensor measurements and perceptual aliasing due to repetitive structures in the environment. In this thesis, we have proposed novel simultaneous localization and mapping (SLAM) algorithms which are more robust to place recognition and sensor errors. Our methods can recover from errors resulting from visual aliasing due to ambiguity in the environment. Additionally, our methods are more robust to gross non-Gaussian errors in sensor mea-surements. As map building can be cast as an optimization problem, mapping algorithm