Fast and automatic city-scale environment modeling for an accurate 6DOF vehicle localization

Larnaout, Dorra; Gripon, Vincent; Bourgeois, Stève; Labbé, Benjamin; Dhome, Michel

doi:10.1109/ismar.2013.6671797

Cited by 4 publications

(1 citation statement)

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“…The rotation around the x-(roll) and y-(pitch) axes can be calculated from the accelerometer's raw data [37]. Because both gyros and accelerometers are affected by errors, they usually fuse to compensate for the weaknesses of each other [104].…”

Section: Inertial Sensor Trackingmentioning

confidence: 99%

A Survey of Marker-Less Tracking and Registration Techniques for Health & Environmental Applications to Augmented Reality and Ubiquitous Geospatial Information Systems

Sadeghi‐Niaraki

Choi

2020

Sensors

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Most existing augmented reality (AR) applications are suitable for cases in which only a small number of real world entities are involved, such as superimposing a character on a single surface. In this case, we only need to calculate pose of the camera relative to that surface. However, when an AR health or environmental application involves a one-to-one relationship between an entity in the real-world and the corresponding object in the computer model (geo-referenced object), we need to estimate the pose of the camera in reference to a common coordinate system for better geo-referenced object registration in the real-world. New innovations in developing cheap sensors, computer vision techniques, machine learning, and computing power have helped to develop applications with more precise matching between a real world and a virtual content. AR Tracking techniques can be divided into two subcategories: marker-based and marker-less approaches. This paper provides a comprehensive overview of marker-less registration and tracking techniques and reviews their most important categories in the context of ubiquitous Geospatial Information Systems (GIS) and AR focusing to health and environmental applications. Basic ideas, advantages, and disadvantages, as well as challenges, are discussed for each subcategory of tracking and registration techniques. We need precise enough virtual models of the environment for both calibrations of tracking and visualization. Ubiquitous GISs can play an important role in developing AR in terms of providing seamless and precise spatial data for outdoor (e.g., environmental applications) and indoor (e.g., health applications) environments.

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Section: Inertial Sensor Trackingmentioning

confidence: 99%