NDLI: Vehicle localization by matching triangulated point patterns

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Vehicle localization by matching triangulated point patterns

Content Provider	ACM Digital Library
Author	Haunert, Jan-Henrik Brenner, Claus
Abstract	We consider the problem of localizing a moving vehicle based on landmarks that were detected with a vehicle-mounted sensor. Landmarks are represented as points; correspondences of these points with the ones in a reference database are searched based on their geometric configurations. More specifically, we triangulate the landmark points and we match the obtained triangles with triangles in a reference database according to their geometric similarity. We maximize the number of triangle matches while considering the topological relations between different triangles, for example, if two triangles share an edge then the corresponding reference triangles must share an edge. Our method exploits that the observed points typically form a certain configuration: They appear at a limited distance from the vehicle's trajectory, thus the typical point pattern has a large extent in the driving direction and a relatively small lateral extent. This characteristic allows us to triangulate the observed point set such that we obtain a triangle strip (a sequence of triangles) in which each two consecutive triangles share one edge and each triangle connects three points that are relatively close to each other, that is, the triangle strip appropriately defines a neighborhood relationship for the landmarks. The adjacency graph of the triangles becomes a path; this allows for an efficient solution of our matching problem by dynamic programming. We present results of our method with data acquired with a mobile laser scanning system. The landmarks are objects of cylindric shape, for example, poles of traffic signs, which can be easily detected with the employed sensor. We tested the method with respect to its running time and its robustness when imposing different types of errors on the data. In particular, we tested the effect of non-rigid distortions of the observed point set, which are typically encountered during dead reckoning. Our matching approach copes well with such errors since it is based on local similarity measures of triangles, that is, we do not assume that a global non-rigid transformation between the observed point set and the reference point set exists.
Starting Page	344
Ending Page	351
Page Count	8
File Format	PDF
ISBN	9781605586496
DOI	10.1145/1653771.1653819
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2009-11-04
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Positioning Point pattern matching Dynamic programming
Content Type	Text
Resource Type	Article

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