Zobrazeno 11 - 20
of 829
pro vyhledávání: '"Willis, Andrew"'
This article describes a technique to augment a typical RGBD sensor by integrating depth estimates obtained via Structure-from-Motion (SfM) with sensor depth measurements. Limitations in the RGBD depth sensing technology prevent capturing depth measu
Externí odkaz:
http://arxiv.org/abs/2103.06366
Autor:
Willis, Andrew R., Brink, Kevin M.
This article describes an algorithm that provides visual odometry estimates from sequential pairs of RGBD images. The key contribution of this article on RGBD odometry is that it provides both an odometry estimate and a covariance for the odometry pa
Externí odkaz:
http://arxiv.org/abs/2103.06236
High energy impacts at joint locations often generate highly fragmented, or comminuted, bone fractures. Current approaches for treatment require physicians to decide how to classify the fracture within a hierarchy fracture severity categories. Each c
Externí odkaz:
http://arxiv.org/abs/2102.11684
This article introduces an approach to facilitate cooperative exploration and mapping of large-scale, near-ground, underground, or indoor spaces via a novel integration framework for locally-dense agent map data. The effort targets limited Size, Weig
Externí odkaz:
http://arxiv.org/abs/2005.10310
This article describes a new approach for distributed 3D SLAM map building. The key contribution of this article is the creation of a distributed graph-SLAM map-building architecture responsive to bandwidth and computational needs of the robotic plat
Externí odkaz:
http://arxiv.org/abs/2005.10222
Synthetic Aperture Radar (SAR) is a critical sensing technology that is notably independent of the sensor-to-target distance and has numerous cross-cutting applications, e.g., target recognition, mapping, surveillance, oceanography, geology, forestry
Externí odkaz:
http://arxiv.org/abs/2005.09736
We propose the width-resolution mutual learning method (MutualNet) to train a network that is executable at dynamic resource constraints to achieve adaptive accuracy-efficiency trade-offs at runtime. Our method trains a cohort of sub-networks with di
Externí odkaz:
http://arxiv.org/abs/1909.12978
Publikováno v:
In Journal of Vascular and Interventional Radiology June 2023 34(6):1015-1021
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