TY - GEN

T1 - Scalable estimation of precision maps in a MapReduce framework

AU - Brenner, Claus

PY - 2016/10/31

Y1 - 2016/10/31

N2 - This paper presents a large-scale strip adjustment method for LiDAR mobile mapping data, yielding highly precise maps. It uses several concepts to achieve scalability. First, an efficient graph-based pre-segmentation is used, which directly operates on LiDAR scan strip data, rather than on point clouds. Second, observation equations are obtained from a dense matching, which is formulated in terms of an estimation of a latent map. As a result of this formulation, the number of observation equations is not quadratic, but rather linear in the number of scan strips. Third, the dynamic Bayes network, which results from all observation and condition equations, is partitioned into two sub-networks. Consequently, the estimation matrices for all position and orientation corrections are linear instead of quadratic in the number of unknowns and can be solved very efficiently using an alternating least squares approach. It is shown how this approach can be mapped to a standard key/value MapReduce implementation, where each of the processing nodes operates independently on small chunks of data, leading to essentially linear scalability. Results are demonstrated for a dataset of one billion measured LiDAR points and 278,000 unknowns, leading to maps with a precision of a few millimeters.

AB - This paper presents a large-scale strip adjustment method for LiDAR mobile mapping data, yielding highly precise maps. It uses several concepts to achieve scalability. First, an efficient graph-based pre-segmentation is used, which directly operates on LiDAR scan strip data, rather than on point clouds. Second, observation equations are obtained from a dense matching, which is formulated in terms of an estimation of a latent map. As a result of this formulation, the number of observation equations is not quadratic, but rather linear in the number of scan strips. Third, the dynamic Bayes network, which results from all observation and condition equations, is partitioned into two sub-networks. Consequently, the estimation matrices for all position and orientation corrections are linear instead of quadratic in the number of unknowns and can be solved very efficiently using an alternating least squares approach. It is shown how this approach can be mapped to a standard key/value MapReduce implementation, where each of the processing nodes operates independently on small chunks of data, leading to essentially linear scalability. Results are demonstrated for a dataset of one billion measured LiDAR points and 278,000 unknowns, leading to maps with a precision of a few millimeters.

KW - Least squares adjustment

KW - LiDAR

KW - MapReduce

KW - Mobile mapping

UR - http://www.scopus.com/inward/record.url?scp=85011018137&partnerID=8YFLogxK

U2 - 10.48550/arXiv.1609.07603

DO - 10.48550/arXiv.1609.07603

M3 - Conference contribution

AN - SCOPUS:85011018137

T3 - GIS: Proceedings of the ACM International Symposium on Advances in Geographic Information Systems

BT - 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2016

A2 - Renz, Matthias

A2 - Ali, Mohamed

A2 - Newsam, Shawn

A2 - Renz, Matthias

A2 - Ravada, Siva

A2 - Trajcevski, Goce

PB - Association for Computing Machinery (ACM)

T2 - 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2016

Y2 - 31 October 2016 through 3 November 2016

ER -