62,028 research outputs found
The Double Sphere Camera Model
Vision-based motion estimation and 3D reconstruction, which have numerous
applications (e.g., autonomous driving, navigation systems for airborne devices
and augmented reality) are receiving significant research attention. To
increase the accuracy and robustness, several researchers have recently
demonstrated the benefit of using large field-of-view cameras for such
applications. In this paper, we provide an extensive review of existing models
for large field-of-view cameras. For each model we provide projection and
unprojection functions and the subspace of points that result in valid
projection. Then, we propose the Double Sphere camera model that well fits with
large field-of-view lenses, is computationally inexpensive and has a
closed-form inverse. We evaluate the model using a calibration dataset with
several different lenses and compare the models using the metrics that are
relevant for Visual Odometry, i.e., reprojection error, as well as computation
time for projection and unprojection functions and their Jacobians. We also
provide qualitative results and discuss the performance of all models
Pop-up SLAM: Semantic Monocular Plane SLAM for Low-texture Environments
Existing simultaneous localization and mapping (SLAM) algorithms are not
robust in challenging low-texture environments because there are only few
salient features. The resulting sparse or semi-dense map also conveys little
information for motion planning. Though some work utilize plane or scene layout
for dense map regularization, they require decent state estimation from other
sources. In this paper, we propose real-time monocular plane SLAM to
demonstrate that scene understanding could improve both state estimation and
dense mapping especially in low-texture environments. The plane measurements
come from a pop-up 3D plane model applied to each single image. We also combine
planes with point based SLAM to improve robustness. On a public TUM dataset,
our algorithm generates a dense semantic 3D model with pixel depth error of 6.2
cm while existing SLAM algorithms fail. On a 60 m long dataset with loops, our
method creates a much better 3D model with state estimation error of 0.67%.Comment: International Conference on Intelligent Robots and Systems (IROS)
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Reflectance Transformation Imaging (RTI) System for Ancient Documentary Artefacts
This tutorial summarises our uses of reflectance transformation imaging in archaeological contexts. It introduces the UK AHRC funded project reflectance Transformation Imaging for Anciant Documentary Artefacts and demonstrates imaging methodologies
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