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DeepVO: Towards end-to-end visual odometry with deep Recurrent Convolutional Neural Networks
TLDR
This paper presents a novel end-to-end monocular VO algorithm based on Deep Learning based on deep Recurrent Convolutional Neural Networks. Expand
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VidLoc: A Deep Spatio-Temporal Model for 6-DoF Video-Clip Relocalization
TLDR
We use temporal smoothness of image-sequences to improve the accuracy of 6-DoF camera re-localization. Expand
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End-to-end, sequence-to-sequence probabilistic visual odometry through deep neural networks
TLDR
An end-to-end, sequence- to-sequence probabilistic visual odometry framework is proposed for the monocular VO based on deep recurrent convolutional neural networks. Expand
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RandLA-Net: Efficient Semantic Segmentation of Large-Scale Point Clouds
  • Q. Hu, B. Yang, +5 authors Andrew Markham
  • Computer Science, Engineering
  • IEEE/CVF Conference on Computer Vision and…
  • 25 November 2019
TLDR
We introduce RandLA-Net, an efficient and lightweight neural architecture to directly infer per-point semantics for large-scale point clouds. Expand
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Learning Object Bounding Boxes for 3D Instance Segmentation on Point Clouds
TLDR
We propose a novel, conceptually simple and general framework for instance segmentation on 3D point clouds. Expand
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Delay-bounded routing in vehicular ad-hoc networks
TLDR
This paper focuses on the development of carry-and-forward schemes that attempt to deliver data from vehicles to fixed infrastructure nodes in an urban setting, while adhering to delay constraints imposed by the application. Expand
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VINet: Visual-Inertial Odometry as a Sequence-to-Sequence Learning Problem
TLDR
In this paper we present an end-to-end trainable method for visual-inertial odometry which performs fusion of the data at an intermediate feature-representation level. Expand
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Supporting Search and Rescue Operations with UAVs
  • S. Waharte, A. Trigoni
  • Computer Science
  • International Conference on Emerging Security…
  • 6 September 2010
TLDR
We study the performance of different search algorithms when the time to find the victim is the optimization criterion and present some of the research avenues we have been exploring. Expand
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Lightweight map matching for indoor localisation using conditional random fields
TLDR
We present MapCraft, a novel, robust and responsive technique that is extremely computationally efficient (running in under 10 ms on an Android smartphone), does not require training in different sites, and tracks well even when presented with very noisy sensor data. Expand
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The Cougar Project: a work-in-progress report
TLDR
We present an update on the status of the Cougar Sensor Database Project, in which we are investigating a database approach to sensor networks: Clients "program" the sensors through queries in a high-level declarative language (such as a variant of SQL). Expand
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