DocumentCode :
3681961
Title :
Conditional Monte Carlo Dense Occupancy Tracker
Author :
Lukas Rummelhard; Nègre;Christian Laugier
fYear :
2015
Firstpage :
2485
Lastpage :
2490
Abstract :
Proper modeling of dynamic environments is a core task in the field of intelligent vehicles. The most common approaches involve the modeling of moving objects, through Detection And Tracking of Moving Objects (DATMO) methods. An alternative to a classic object model framework is the occupancy grid filtering domain. Instead of segmenting the scene into objects and track them, the environment is represented as a regular grid of occupancy, in which spatial occupancy is tracked at a sub-object level. In this paper, we present the Conditional Monte Carlo Dense Occupancy Tracker, a generic spatial occupancy tracker, which infers dynamics of the scene through an hybrid representation of the environment, consisting of static occupancy, dynamic occupancy, empty spaces and unknown areas. This differentiation enables the use of state specific models (classic occupancy grid for motion-less components, set of moving particles for dynamic occupancy) as well as proper confidence estimation and management of data-less areas. The approach leads to a compact model that drastically improves the accuracy of the results and the global efficiency in comparison to previous methods.
Keywords :
"Vehicle dynamics","Bayes methods","Dynamics","Estimation","Mathematical model","Robot sensing systems","Computational modeling"
Publisher :
ieee
Conference_Titel :
Intelligent Transportation Systems (ITSC), 2015 IEEE 18th International Conference on
ISSN :
2153-0009
Electronic_ISBN :
2153-0017
Type :
conf
DOI :
10.1109/ITSC.2015.400
Filename :
7313492
Link To Document :
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