A. Trigoni

Publications
Influence

DeepVO: Towards end-to-end visual odometry with deep Recurrent Convolutional Neural Networks

S. Wang, Ronald Clark, Hongkai Wen, A. Trigoni
Computer Science
IEEE International Conference on Robotics and…
15 January 2017

This paper presents a novel end-to-end monocular VO algorithm based on Deep Learning based on deep Recurrent Convolutional Neural Networks. Expand

VidLoc: A Deep Spatio-Temporal Model for 6-DoF Video-Clip Relocalization

Ronald Clark, S. Wang, A. Markham, A. Trigoni, Hongkai Wen
Computer Science
IEEE Conference on Computer Vision and Pattern…
21 February 2017

We use temporal smoothness of image-sequences to improve the accuracy of 6-DoF camera re-localization. Expand

End-to-end, sequence-to-sequence probabilistic visual odometry through deep neural networks

S. Wang, Ronald Clark, Hongkai Wen, A. Trigoni
Computer Science
Int. J. Robotics Res.
1 April 2018

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

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

We introduce RandLA-Net, an efficient and lightweight neural architecture to directly infer per-point semantics for large-scale point clouds. Expand

Learning Object Bounding Boxes for 3D Instance Segmentation on Point Clouds

Bo Yang, Jianan Wang, +4 authors A. Trigoni
Computer Science
NeurIPS
4 June 2019

We propose a novel, conceptually simple and general framework for instance segmentation on 3D point clouds. Expand

Delay-bounded routing in vehicular ad-hoc networks

Antonios Skordylis, A. Trigoni
Computer Science
MobiHoc '08
26 May 2008

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

VINet: Visual-Inertial Odometry as a Sequence-to-Sequence Learning Problem

Ronald Clark, S. Wang, Hongkai Wen, A. Markham, A. Trigoni
Computer Science
AAAI
29 January 2017

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

Supporting Search and Rescue Operations with UAVs

S. Waharte, A. Trigoni
Computer Science
International Conference on Emerging Security…
6 September 2010

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

Lightweight map matching for indoor localisation using conditional random fields

Zhuoling Xiao, Hongkai Wen, A. Markham, A. Trigoni
Computer Science
IPSN-14 Proceedings of the 13th International…
15 April 2014

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

The Cougar Project: a work-in-progress report

A. Demers, J. Gehrke, R. Rajaraman, A. Trigoni, Y. Yao
Computer Science
SGMD
1 December 2003

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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