Name
Abstract | ||
|---|---|---|
Obtaining a comprehensive model of large and complex ground typically is crucial for autonomous driving both in urban and countryside environments. This paper presents an improved ground segmentation method for 3D LIDAR point clouds. Our approach builds on a polar grid map, which is divided into some sectors, then 1D Gaussian process (GP) regression model and Incremental Sample Consensus (INSAC) algorithm is used to extract ground for every sector. Experiments are carried out at the autonomous vehicle in different outdoor scenes, and results are compared to those of the existing method. We show that our method can get more promising performance. © 2011 IEEE. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1109/ACPR.2011.6166587 | ACPR |
Keywords | DocType | Volume |
classification algorithms,point clouds,data model,regression model,radar imaging,telerobotics,gaussian processes,regression analysis,laser radar,mobile robots,point cloud,data models,three dimensional,gaussian process,mobile robot,image segmentation | Conference | null |
Issue | ISSN | ISBN |
null | null | 978-1-4577-0122-1 |
Citations | PageRank | References |
9 | 0.59 | 3 |
Authors | ||
5 | ||