Object Geolocation Using MRF Based Multi-Sensor Fusion

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...

Full description

Saved in:
Bibliographic Details
Main Authors: Krylov, Vladimir A., Dahyot, Rozenn
Format: Conference Proceeding
Language:English
Subjects:
Cameras
Geology
Image segmentation
Laser radar
LiDAR data
Markov random fields
Object geolocation
Pipelines
Roads
street level imagery
Three-dimensional displays
traffic lights
Online Access:Request full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:2381-8549
DOI:10.1109/ICIP.2018.8451458