Defence of doctoral thesis in the field of Computer Science, Santiago Cortés Reina

Title of the doctoral thesis is "Models and Methods for Inertial and Visual Odometry"
CS_defence_3 photo by Matti Ahlgren

Smartphones have become a part of everyday modern life. Their presence and capabilities have changed the way a lot of tasks are performed. They have fundamentally altered the approach to navigation, how to find a way from one point to another. Satellite-based navigation allows for easy localization and path planning when traveling outdoors in the open. Recently augmented reality and local mapping have also gained ubiquity. However, medium-scale indoor navigation and mapping have not been reliably solved. Challenges in these environments are characterized by an unreliable satellite signal and visual features which are not easily recognizable or not revisited.

This thesis is focused on inertial and visual-inertial navigation for pedestrian dead reckoning. The systems, algorithms, datasets, and benchmarks proposed in this thesis are all designed with smartphones and pedestrians in mind. The central statement of the thesis is that careful consideration of uncertainty sources, the characteristics of pedestrian motion, and the quality of the available signals allows for a system that produces reliable odometry over medium-length sequences of pedestrian motion.

Two sensor modalities are explored in this thesis, inertial and visual-inertial odometry. Pure inertial odometry relies on complementary signals to keep a reliable estimate of the pose. Multiple approaches to utilize these complementary signals are explored in this thesis. Visual-inertial odometry is approached with computational efficiency in mind. The measurements from the camera and the inertial sensors are integrated using a state space model driven by inertial odometry. This is to allow for efficient use of camera information and complementary online processing. This thesis also introduces a dataset which is a representative sample of the kind of use cases that are problematic for most available systems. This dataset is used in both modalities to train, evaluate and benchmark results in pedestrian dead reckoning.

The models and methods discussed in this thesis are the base of many modern applications, from AR to self driving cars. Improvements in these building blocks open possibilities for better and less computationally intensive applications. Furthermore the possibility of tracking "in the pocket" can change pedestrian navigation in satellite deprived areas.

Opponent: Dr. Michael Blösch, DeepMind Technologies, London, United Kingdom.

Custos: Professor Arno Solin, Aalto University School of Science, Department of Computer Science

Contact details of the doctoral student: [email protected]

The public defence will be organised via Zoom. Link to the event 

The dissertation is publicly displayed 10 days before the defence in the publication archive Aaltodoc of Aalto University. 

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