THESIS
2022
1 online resource (xiv, 116 pages) : illustrations (chiefly color)
Abstract
State estimation is a crucial building block for an autonomous navigation system with
high intelligence. Techniques for state estimation enable an agent to perceive geometric information
related to itself and its surroundings, e.g., sensor pose tracking, self-localization,
and mapping. For run-time robustness and precision, generally, robots are equipped with
heterogeneous sensors to guarantee necessary information redundancy, which requires the
state estimation module to fully exploit the advantages of individual sensors while fusing
the perceptual data in a complementary manner.
This thesis presents a scalable state estimation system via dense geometry modelling
and cross-modal visual localization. We begin with reconstruction from ranging sensors
that provide the dense geometric stru...[
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State estimation is a crucial building block for an autonomous navigation system with
high intelligence. Techniques for state estimation enable an agent to perceive geometric information
related to itself and its surroundings, e.g., sensor pose tracking, self-localization,
and mapping. For run-time robustness and precision, generally, robots are equipped with
heterogeneous sensors to guarantee necessary information redundancy, which requires the
state estimation module to fully exploit the advantages of individual sensors while fusing
the perceptual data in a complementary manner.
This thesis presents a scalable state estimation system via dense geometry modelling
and cross-modal visual localization. We begin with reconstruction from ranging sensors
that provide the dense geometric structure for the localization system. We first propose
a multi-view point cloud registration method with point-set bundle adjustment, which
simultaneously refines the poses of individual scans in an alternating optimization framework.
With a deformable map representation, we further propose an efficient and globally
consistent odometry and mapping system for typical LiDARs, which is capable of providing
geometric priors in environments with different scales.
In the next stage, we explore the cross-modal localization method as an intermediate
way of combining the advantages of visual and geometric measurements. The basic
framework associates the sparse visual structure with the pre-built geometric structure,
and then introduces the structure regulation in the optimization to eliminate the drift
and align the coordinate globally. To this end, we propose a monocular localization system based on the Signed Distance Field (SDF), which could robustly initialize with the
pre-built dense map and consistently track the camera. Finally, to make the system more
applicable, we further model the scene geometry as Gaussian Mixture Model (GMM)
and introduce the geometric information into the visual localization system. Overall, this
thesis presents a scalable pipeline that reconstructs the geometry of the environment and
then tracks cameras over the pre-built dense map.
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