Vehicle localization using in-vehicle pitch data and dynamical models

Research output: Contribution to journalArticle

16 Citations (Scopus)

Abstract

This paper describes a dynamical model-based method for the localization of road vehicles using terrain data from the vehicle's onboard sensors. Road data are encoded using linear dynamical models and then, during travel, the location is identified through continuous comparison of a bank of linear models. The approach presented has several advantages over previous methods described in the literature. First, it creates computationally efficient linear model map representations of the road data. Second, the use of linear models eliminates the need for metrics during the localization process. Third, the localization algorithm is a computationally efficient approach that can have a bounded localization distance in the absence of noise, given certain uniqueness assumptions on the data. Fourth, encoding road data using linear models has the potential to compress the data, while retaining the sensory information. Finally, performing only linear operations on observed noisy data simplifies the creation of noise mitigation algorithms.

Original languageEnglish (US)
Article number6863698
Pages (from-to)206-220
Number of pages15
JournalIEEE Transactions on Intelligent Transportation Systems
Volume16
Issue number1
DOIs
StatePublished - Feb 1 2015

Fingerprint

Sensors

All Science Journal Classification (ASJC) codes

  • Automotive Engineering
  • Mechanical Engineering
  • Computer Science Applications

Cite this

@article{b715b6ca6272488096153d764887f12d,
title = "Vehicle localization using in-vehicle pitch data and dynamical models",
abstract = "This paper describes a dynamical model-based method for the localization of road vehicles using terrain data from the vehicle's onboard sensors. Road data are encoded using linear dynamical models and then, during travel, the location is identified through continuous comparison of a bank of linear models. The approach presented has several advantages over previous methods described in the literature. First, it creates computationally efficient linear model map representations of the road data. Second, the use of linear models eliminates the need for metrics during the localization process. Third, the localization algorithm is a computationally efficient approach that can have a bounded localization distance in the absence of noise, given certain uniqueness assumptions on the data. Fourth, encoding road data using linear models has the potential to compress the data, while retaining the sensory information. Finally, performing only linear operations on observed noisy data simplifies the creation of noise mitigation algorithms.",
author = "Laftchiev, {Emil I.} and Lagoa, {Constantino Manuel} and Brennan, {Sean N.}",
year = "2015",
month = "2",
day = "1",
doi = "10.1109/TITS.2014.2330795",
language = "English (US)",
volume = "16",
pages = "206--220",
journal = "IEEE Transactions on Intelligent Transportation Systems",
issn = "1524-9050",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
number = "1",

}

Vehicle localization using in-vehicle pitch data and dynamical models. / Laftchiev, Emil I.; Lagoa, Constantino Manuel; Brennan, Sean N.

In: IEEE Transactions on Intelligent Transportation Systems, Vol. 16, No. 1, 6863698, 01.02.2015, p. 206-220.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Vehicle localization using in-vehicle pitch data and dynamical models

AU - Laftchiev, Emil I.

AU - Lagoa, Constantino Manuel

AU - Brennan, Sean N.

PY - 2015/2/1

Y1 - 2015/2/1

N2 - This paper describes a dynamical model-based method for the localization of road vehicles using terrain data from the vehicle's onboard sensors. Road data are encoded using linear dynamical models and then, during travel, the location is identified through continuous comparison of a bank of linear models. The approach presented has several advantages over previous methods described in the literature. First, it creates computationally efficient linear model map representations of the road data. Second, the use of linear models eliminates the need for metrics during the localization process. Third, the localization algorithm is a computationally efficient approach that can have a bounded localization distance in the absence of noise, given certain uniqueness assumptions on the data. Fourth, encoding road data using linear models has the potential to compress the data, while retaining the sensory information. Finally, performing only linear operations on observed noisy data simplifies the creation of noise mitigation algorithms.

AB - This paper describes a dynamical model-based method for the localization of road vehicles using terrain data from the vehicle's onboard sensors. Road data are encoded using linear dynamical models and then, during travel, the location is identified through continuous comparison of a bank of linear models. The approach presented has several advantages over previous methods described in the literature. First, it creates computationally efficient linear model map representations of the road data. Second, the use of linear models eliminates the need for metrics during the localization process. Third, the localization algorithm is a computationally efficient approach that can have a bounded localization distance in the absence of noise, given certain uniqueness assumptions on the data. Fourth, encoding road data using linear models has the potential to compress the data, while retaining the sensory information. Finally, performing only linear operations on observed noisy data simplifies the creation of noise mitigation algorithms.

UR - http://www.scopus.com/inward/record.url?scp=84922519278&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84922519278&partnerID=8YFLogxK

U2 - 10.1109/TITS.2014.2330795

DO - 10.1109/TITS.2014.2330795

M3 - Article

VL - 16

SP - 206

EP - 220

JO - IEEE Transactions on Intelligent Transportation Systems

JF - IEEE Transactions on Intelligent Transportation Systems

SN - 1524-9050

IS - 1

M1 - 6863698

ER -