Data-Driven Models for Robust Egomotion Estimation

In many modern autonomy applications, robots are required to operate safely and reliably within complex environments, alongside other dynamic agents such as humans. To meet these requirements, localization algorithms for robots and humans must be developed that can maintain accurate pose estimates, despite being subjected to a range of adverse operating conditions. Further, the development of self-localization algorithms that enable mobile agents to maintain an estimate of their own pose is particularly important for improved autonomy. At the heart of self-localization is egomotion estimation, which is the process of determining the motion of a mobile agent over time using a stream of body-mounted sensor measurements. Body-mounted sensors such as cameras and inertial measurement units are self-contained, lightweight, and inexpensive, making them ideal candidates for self-localization. Traditional approaches to egomotion estimation are based on handcrafted models that achieve a high degree of accuracy while operating under a range of nominal conditions, but are prone to failure when the assumptions no longer hold. In this dissertation, we investigate how data-driven, or learned, models can be leveraged within the egomotion estimation pipeline to improve upon existing classical approaches. In particular, we develop a number of hybrid and end-to-end systems for inertial and visual egomotion estimation. The hybrid systems replace brittle components of classical egomotion estimators with data-driven models, while the end-to-end systems solely use neural networks that are trained to directly map from sensor data to egomotion predictions. We employ these data-driven systems for self-localization in pedestrian navigation, urban driving, and unmanned aerial vehicle applications. In these domains, we benchmark our systems on several real-world datasets, including a pedestrian navigation dataset that we collected at the University of Toronto. Our experiments demonstrate that, in challenging environments where classical estimation frameworks fail, data-driven systems are viable candidates for maintaining self-localization accuracy.

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