Learning-based geometry-aware ego-motion estimation - Almalioglu, Y (2021)
Precise ego-motion estimation is a fundamental component for numerous applications in robotics, autonomous driving, virtual/augmented reality, and mobile computing. Although most navigation systems heavily rely on signals from space-based Global Navigation Satellite Systems (GNSS), such as GPS, to estimate position, radio signals can be lost or degraded in many environments due to obstruction or reflection. In particular, operation in urban areas surrounded by high-rise buildings, underground, and indoor environments remains highly challenging. Moreover, GNSS typically only provides metre-level location accuracy without orientation information, rendering it poorly suited to high-precision applications. Ego-motion estimation with internal sensors such as cameras, inertial measurement units (IMUs), lidars and radars provide an alternative to obtaining accurate relative self-position in these challenging environments. A robust and reliable ego-motion estimation system should address the vulnerabilities of the sensors that might be caused by several factors, including occlusions, illumination changes, sensor noise, and multipath effects. Over the past decades, researchers have developed approaches based on various methodologies, including traditional and learning-based methods, to address this challenging problem, also known as odometry. Although learning-based approaches alleviate the issues such as the need for hand-crafted mathematical features and strict parameter tuning typically required for traditional methods, the acquisition of accurate ground-truth data to supervise the learning-based systems is expensive and limited due to the need for existing infrastructure and deficiencies of the existing sensors. Moreover, supervised methods suffer from poor generalization performance in new challenging environments that are unobserved during the training. Neither the traditional approaches nor the current research solutions meet all the requirements needed for a robust and reliable solution in such demanding conditions. In this thesis, we address specific challenges of ego-motion estimation that pertain to multiple sensor modalities, principally exploiting the geometric constraints of the scene and unlabelled data.