Show simple item record

AuthorNuss, Dominikdc.contributor.author
Date of accession2017-06-01T09:02:54Zdc.date.accessioned
Available in OPARU since2017-06-01T09:02:54Zdc.date.available
Year of creation2016dc.date.created
Date of first publication2017-06-01dc.date.issued
AbstractReliable vehicle environment perception is a basic prerequisite for advanced driver assistant systems and autonomously driving cars. A common environment representation form is an occupancy grid map. It divides the environment into single grid cells and estimates for each cell whether the space it represents is occupied or free, assuming grid cells are independent of each other. The mathematical framework is based on the binary Bayes filter (BBF), which combines sensor measurements from different sensors and potentially from different points in time. Since an occupancy grid map does not employ a concept of individual objects, it is able to represent arbitrarily shaped obstacles. A classical occupancy grid map is not eligible for estimating dynamic environments, because it does not apply a process model. A much-noticed extension to a static occupancy grid map is the Bayesian occupancy filter (BOF). In contrast to a classical occupancy grid, the BOF estimates a velocity distribution for the occupancy of each grid cell based on a histogram filter. Since the BOF is computationally extremely demanding, recent publications suggest to represent the dynamic state of grid cells with particles. This allows to calculate dynamic grid maps in real-time applications with increased grid cell size and resolution. Today, dynamic occupancy grid maps are still a younger research area and not as well-studied as object-tracking approaches are. Up to now, the BOF has been addressed as a research field with little connection to other tracking methods. This work presents a new concept of dynamic grid mapping as an approximation of a random finite set (RFS) filter. A random finite set is a general, probabilistic representation of a random but limited number of objects and their states. The finite set statistics (FISST) describe Bayesian filtering of random finite sets and are basis for a number of multi-object tracking approaches like the probability hypothesis density (PHD) filter. Describing the grid as a random finite set allows transferring advanced concepts from the well-established field of random finite set filtering to the field of dynamic grid mapping. The thesis derives a filter called probability hypothesis density / multi-instance Bernoulli (PHD/MIB) filter, which represents and propagates the dynamic grid map in alternating forms as a PHD and as multiple instances of Bernoulli filters. Additionally, the thesis presents a sequential Monte Carlo (SMC) realization of the PHD/MIB filter and an approximation in the Dempster-Shafer domain called Dempster-Shafer PHD/MIB (DS-PHD/MIB) filter, which requires a smaller number of particles than the original PHD/MIB filter. The thesis describes in detail an efficient, massively parallel implementation of the DS-PHD/MIB filter and outlines the algorithm in pseudo code. Finally, the thesis describes characteristics of the DS-PHD/MIB filter and discusses its advantages and disadvantages compared to object-based tracking approaches using practical application examples. A quantitative evaluation with real-world data shows that the DS-PHD/MIB filter provides consistent state estimation results and that it appropriately models the stochastic multi-object transition process and the stochastic multi-object observation process. Furthermore, the evaluation confirms the real-time capability of the parallelized implementation of the DS-PHD/MIB filter and its usefulness for state estimation of a dynamic vehicle environment.dc.description.abstract
AbstractA short version of the thesis has been submitted for publication in The International Journal of Robotics Research and has been made available to the public via arXiv: Nuss, Dominik; Reuter, Stephan; Thom, Markus; Yuan, Ting; Krehl, Gunther; Maile, Michael; Gern, Axel; Dietmayer, Klaus: A random finite set approach for dynamic occupancy grid maps with real-time application. In: ArXiv e-prints, 2016. Available online at http://arxiv.org/abs/1605.02406.dc.description.abstract
Languageen_USdc.language.iso
PublisherUniversität Ulmdc.publisher
LicenseStandard (ohne Print-on-Demand)dc.rights
Link to license texthttps://oparu.uni-ulm.de/xmlui/license_opod_v1dc.rights.uri
KeywordSelf-driving carsdc.subject
KeywordObject trackingdc.subject
KeywordSensor data fusiondc.subject
KeywordRandom finite setsdc.subject
KeywordEnvironment perceptiondc.subject
Dewey Decimal GroupDDC 620 / Engineering & allied operationsdc.subject.ddc
LCSHRoboticsdc.subject.lcsh
LCSHAutonomous vehiclesdc.subject.lcsh
LCSHDriver assistance systemsdc.subject.lcsh
TitleA random finite set approach for dynamic occupancy grid mapsdc.title
Resource typeDissertationdc.type
Date of acceptance2016-12-16dcterms.dateAccepted
RefereeDietmayer, Klausdc.contributor.referee
RefereeKoch, Wolfgangdc.contributor.referee
DOIhttp://dx.doi.org/10.18725/OPARU-4361dc.identifier.doi
PPN890547289dc.identifier.ppn
URNhttp://nbn-resolving.de/urn:nbn:de:bsz:289-oparu-4400-4dc.identifier.urn
GNDRobotikdc.subject.gnd
GNDObjektverfolgungdc.subject.gnd
GNDAutonomes Fahrzeugdc.subject.gnd
GNDFahrerassistenzsystemdc.subject.gnd
FacultyFakultät für Ingenieurwissenschaften, Informatik und Psychologieuulm.affiliationGeneral
InstitutionInstitut für Mess-, Regel- und Mikrotechnikuulm.affiliationSpecific
Shelfmark print versionW: W-H 14.382uulm.shelfmark
Grantor of degreeFakultät für Ingenieurwissenschaften, Informatik und Psychologieuulm.thesisGrantor
DCMI TypeTextuulm.typeDCMI
TypeErstveröffentlichunguulm.veroeffentlichung
CategoryPublikationenuulm.category
Ulm seriesSchriftenreihe des Instituts für Mess-, Regel- und Mikrotechnikuulm.dissSeriesUlmName
Ulm series - number20uulm.dissSeriesUlmNumber
Bibliographyuulmuulm.bibliographie


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record