Robust map design by outlier point selection for terrain-based vehicle localization

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Scopus citations


Using terrain data and dynamical models is a promising approach to map-based passenger vehicle localization. In this approach, dynamical models are extracted from terrain data collected by a vehicle with a known location. The dynamical models are stored as a "map" of the data onto other vehicles. These vehicles can then discern their own location by comparing the newly acquired terrain data against the preextracted models. This approach has been shown to be an effective method of localization. However, system noise remains a significant challenge, affecting both model extraction and localization. This paper introduces a novel approach to model extraction that maximizes the robustness of the extracted model map to inertial measurement unit noise. Three mechanisms are employed. First, the model map is represented as a tiered tree, with models describing successively finer data decimations in lower tree levels. Second, during the extraction process, the models and the transitions between models are chosen to accentuate the outlier end point that denotes the transition event. Finally, the extracted models are forced to have specific properties that address the noise added by the inertial measurement unit. An additional benefit of the presented algorithm is that it generates model maps independently given a fixed model order. This provides a convenient method of efficiently adding new information to the vehicle's map. The approach is tested using vehicle pitch data collected in State College, Pennsylvania USA.

Original languageEnglish (US)
Title of host publication2013 IEEE 52nd Annual Conference on Decision and Control, CDC 2013
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
ISBN (Print)9781467357173
StatePublished - 2013
Event52nd IEEE Conference on Decision and Control, CDC 2013 - Florence, Italy
Duration: Dec 10 2013Dec 13 2013

Publication series

NameProceedings of the IEEE Conference on Decision and Control
ISSN (Print)0743-1546
ISSN (Electronic)2576-2370


Other52nd IEEE Conference on Decision and Control, CDC 2013

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Modeling and Simulation
  • Control and Optimization


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