Name
Abstract | ||
|---|---|---|
We present a method to compute an initial alignment for pairwise registration of point clouds. This method uses the properties of a rigid body transformation - the ratio of lengths is preserved, the euclidean distance between points is preserved - to find congruent pyramids in two point clouds. The corresponding vertices of the congruent pyramids are used to derive a closed form solution for initial alignment. The alignment is refined further using the Iterative Closest Point algorithm. We validate the method on challenging datasets - which include airborne LIDAR, outdoor, and indoor - having initial offsets and varying densities. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/IROS.2014.6942800 | Intelligent Robots and Systems |
Keywords | Field | DocType |
airborne radar,optical radar,Euclidean distance,airborne LIDAR,congruent pyramids,iterative closest point algorithm,pairwise registration,point cloud registration,rigid body transformation | Computer vision,Computer science,Artificial intelligence,Point cloud,Congruence (geometry),Iterative closest point | Conference |
ISSN | Citations | PageRank |
2153-0858 | 1 | 0.39 |
References | Authors | |
11 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Aravindhan K. Krishnan | 1 | 1 | 1.41 |
| Srikanth Saripalli | 2 | 564 | 60.11 |