Road Intensity Based Mapping using Radar Measurements with a Probability Hypothesis Density Filter
2011 (English)In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 59, no 4, 1397-1408 p.Article in journal (Refereed) Published
Mapping stationary objects is essential for autonomous vehicles and many autonomous functions in vehicles. In this contribution the probability hypothesis density (PHD) filter framework is applied to automotive imagery sensor data for constructing such a map, where the main advantages are that it avoids the detection, the data association and the track handling problems in conventional multiple-target tracking, and that it gives a parsimonious representation of the map in contrast to grid based methods. Two original contributions address the inherent complexity issues of the algorithm: First, a data clustering algorithm is suggested to group the components of the PHD into different clusters, which structures the description of the prior and considerably improves the measurement update in the PHD filter. Second, a merging step is proposed to simplify the map representation in the PHD filter. The algorithm is applied to multi-sensor radar data collected on public roads, and the resulting map is shown to well describe the environment as a human perceives it.
Place, publisher, year, edition, pages
IEEE Signal Processing Society, 2011. Vol. 59, no 4, 1397-1408 p.
Clustering, Gaussian mixture, PHD, mapping, probability hypothesis density, road edge estimation
Signal Processing Control Engineering
IdentifiersURN: urn:nbn:se:liu:diva-66449DOI: 10.1109/TSP.2010.2103065ISI: 000290810100006OAI: oai:DiVA.org:liu-66449DiVA: diva2:404125
ProjectsIVSS - SEFSCADICS
©2011 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.2011-03-242011-03-162013-09-23Bibliographically approved