Krylov, Vladimir A and Dahyot, Rozenn (2018) Object Geolocation Using MRF Based Multi-Sensor Fusion. 2018 25th IEEE International Conference on Image Processing (ICIP). ISSN 2381-8549

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Abstract

Abundant image and sensory data collected over the last decades represents an invaluable source of information for cataloging and monitoring of the environment. Fusion of heterogeneous data sources is a challenging but promising tool to efficiently leverage such information. In this work we propose a pipeline for automatic detection and geolocation of recurring stationary objects deployed on fusion scenario of street level imagery and LiDAR point cloud data. The objects are geolocated coherently using a fusion procedure formalized as a Markov random field problem. This allows us to efficiently combine information from object segmentation, triangulation, monocular depth estimation and position matching with LiDAR data. The proposed fusion approach produces object mappings robust to scenes reporting multiple object instances. We introduce a new challenging dataset of over 200 traffic lights in Dublin city centre and demonstrate high performance of the proposed methodology and its capacity to perform multi -sensor data fusion.

Item Type:	Article
Keywords:	Object geolocation; street level imagery; LiDAR data; Markov random fields; traffic lights;
Academic Unit:	Faculty of Science and Engineering > Research Institutes > Hamilton Institute
Item ID:	15253
Identification Number:	https://doi.org/10.1109/ICIP.2018.8451458
Depositing User:	Rozenn Dahyot
Date Deposited:	17 Jan 2022 16:50
Journal or Publication Title:	2018 25th IEEE International Conference on Image Processing (ICIP)
Publisher:	IEEE
Refereed:	Yes
URI:	Publisher
Use Licence:	This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here

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