TY - GEN
T1 - Visual positioning systems-An extension to MoVIPS
AU - Marouane, Chadly
AU - Maier, Marco
AU - Feld, Sebastian
AU - Werner, Martin
N1 - Publisher Copyright:
© 2014 IEEE.
PY - 2014
Y1 - 2014
N2 - Due to the increasing popularity of location-based services, the need for reliable and cost-effective indoor positioning methods is rising. As an alternative to radio-based localization methods, in 2011, we introduced MoVIPS (Mobile Visual Indoor Positioning System), which is based on the idea to extract visual feature points from a query image and compare them to those of previously collected geo-referenced images. The general feasibility of positioning by SURF points on a conventional smartphone was already shown in our previous work. However, the system still faced several shortcomings concerning real-world usage such as request times being too high and distance estimation being unreliable because of the employed estimation method not being rotation invariant. In this paper, three extensions are presented that improve the practical applicability of MoVIPS. To speed up request times, both a dead reckoning approach (based on step counting using the accelerometer) and an orientation estimation (based on the smartphones compass) are introduced to filter relevant images from the database and thus to reduce the amount of images to compare the query image to. Furthermore, the vectors of the SURF points are quantized. For this purpose, clusters are calculated from all SURF points from the database. As a result, each image can be represented by a histogram of cluster frequencies, which can be compared with each other a lot more efficiently. The third extension is an improvement of the distance estimation method, which uses the matched feature points of an image to perform a perspective transformation and to determine the actual position with the aid of the transformation matrix.
AB - Due to the increasing popularity of location-based services, the need for reliable and cost-effective indoor positioning methods is rising. As an alternative to radio-based localization methods, in 2011, we introduced MoVIPS (Mobile Visual Indoor Positioning System), which is based on the idea to extract visual feature points from a query image and compare them to those of previously collected geo-referenced images. The general feasibility of positioning by SURF points on a conventional smartphone was already shown in our previous work. However, the system still faced several shortcomings concerning real-world usage such as request times being too high and distance estimation being unreliable because of the employed estimation method not being rotation invariant. In this paper, three extensions are presented that improve the practical applicability of MoVIPS. To speed up request times, both a dead reckoning approach (based on step counting using the accelerometer) and an orientation estimation (based on the smartphones compass) are introduced to filter relevant images from the database and thus to reduce the amount of images to compare the query image to. Furthermore, the vectors of the SURF points are quantized. For this purpose, clusters are calculated from all SURF points from the database. As a result, each image can be represented by a histogram of cluster frequencies, which can be compared with each other a lot more efficiently. The third extension is an improvement of the distance estimation method, which uses the matched feature points of an image to perform a perspective transformation and to determine the actual position with the aid of the transformation matrix.
UR - http://www.scopus.com/inward/record.url?scp=84947912525&partnerID=8YFLogxK
U2 - 10.1109/IPIN.2014.7275472
DO - 10.1109/IPIN.2014.7275472
M3 - Conference contribution
AN - SCOPUS:84947912525
T3 - IPIN 2014 - 2014 International Conference on Indoor Positioning and Indoor Navigation
SP - 95
EP - 104
BT - IPIN 2014 - 2014 International Conference on Indoor Positioning and Indoor Navigation
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 5th International Conference on Indoor Positioning and Indoor Navigation, IPIN 2014
Y2 - 27 October 2014 through 30 October 2014
ER -