TY - GEN
T1 - Digital terrain model generation using LiDAR Ground Points
AU - Zang, Andi
AU - Chen, Xin
AU - Trajcevski, Goce P
PY - 2015/11/3
Y1 - 2015/11/3
N2 - As the trend of autonomous self-driving cars is becoming more of a reality, High-quality navigation methods and tools become a paramount. This, in turn, is crucially dependent on High-definition maps, for which one of the enabling tools is high resolution Digital Terrain Model (DTM) - the role and values of which have already been demonstrated even in the settings of manned cars. Traditional DTM generation methods have insurmountable barriers in creating centimeter-level resolution. In this paper, we propose a novel method for fully-automated, high precision DTM generation using the database generated and maintained in our existed dataset, and with no additional overheads in terms of extract labor and equipment cost. The input data is a point cloud captured by the vehiclemount LiDAR devices which, naturally, has extremely large volume. We show how with Ground Points Processing and DTM Generation steps, we can generate a centimeter-resolution DTM and, as our experiments demonstrate, when compared to DTM form U.S. Geological Survey (USGS) and altitude data from a third party surveying dataset, our proposed DTM indeed provides a higher precision.
AB - As the trend of autonomous self-driving cars is becoming more of a reality, High-quality navigation methods and tools become a paramount. This, in turn, is crucially dependent on High-definition maps, for which one of the enabling tools is high resolution Digital Terrain Model (DTM) - the role and values of which have already been demonstrated even in the settings of manned cars. Traditional DTM generation methods have insurmountable barriers in creating centimeter-level resolution. In this paper, we propose a novel method for fully-automated, high precision DTM generation using the database generated and maintained in our existed dataset, and with no additional overheads in terms of extract labor and equipment cost. The input data is a point cloud captured by the vehiclemount LiDAR devices which, naturally, has extremely large volume. We show how with Ground Points Processing and DTM Generation steps, we can generate a centimeter-resolution DTM and, as our experiments demonstrate, when compared to DTM form U.S. Geological Survey (USGS) and altitude data from a third party surveying dataset, our proposed DTM indeed provides a higher precision.
KW - Digital terrain model
KW - GIS
KW - LiDAR
KW - Point cloud processing
UR - http://www.scopus.com/inward/record.url?scp=84980351338&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84980351338&partnerID=8YFLogxK
U2 - 10.1145/2835022.2835024
DO - 10.1145/2835022.2835024
M3 - Conference contribution
AN - SCOPUS:84980351338
T3 - Proceedings of the 1st International ACM SIGSPATIAL Workshop on Smart Cities and Urban Analytics, UrbanGIS 2015
SP - 9
EP - 15
BT - Proceedings of the 1st International ACM SIGSPATIAL Workshop on Smart Cities and Urban Analytics, UrbanGIS 2015
PB - Association for Computing Machinery, Inc
T2 - 1st International ACM SIGSPATIAL Workshop on Smart Cities and Urban Analytics, UrbanGIS 2015
Y2 - 3 November 2015 through 6 November 2015
ER -