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A Chain‐Code‐Based Map Matching Algorithm for Wheelchair Navigation
Author(s) -
Ren Ming,
Karimi Hassan A
Publication year - 2009
Publication title -
transactions in gis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.721
H-Index - 63
eISSN - 1467-9671
pISSN - 1361-1682
DOI - 10.1111/j.1467-9671.2009.01147.x
Subject(s) - map matching , global positioning system , matching (statistics) , computer science , code (set theory) , trajectory , algorithm , position (finance) , chain code , blossom algorithm , tracking (education) , function (biology) , computer vision , data mining , artificial intelligence , mathematics , evolutionary biology , telecommunications , pedagogy , psychology , statistics , physics , set (abstract data type) , finance , astronomy , economics , image (mathematics) , biology , programming language
Accurate vehicle tracking is essential for navigation systems to function correctly. Unfortunately, GPS data is still plagued with errors that frequently produce inaccurate trajectories. Research in map matching algorithms focuses on how to efficiently match GPS tracking data to the underlying road network. This article presents an innovative map matching algorithm that considers the trajectory of the data rather than merely the current position as in the typical map matching case. Instead of computing the precise angle which is traditionally used, a discrete eight‐direction chain code, to represent a trend of movement, is used. Coupled with distance information, map matching decisions are made by comparing the differences between trajectories representing the road segments and GPS tracking data chain‐codes. Moreover, to contrast the performance of the chain‐code algorithm, two evaluation strategies, linear and non‐linear, are analyzed. The presented chain‐code map matching algorithm was evaluated for wheelchair navigation using university campus sidewalk data. The evaluation results indicate that the algorithm is efficient in terms of accuracy and computational time.