Name
Abstract | ||
|---|---|---|
This paper focuses on range image registration for robot localization and environment mapping. It extends the well-known Iterative Closest Point (ICP) algorithm in order to deal with erroneous measurements. The dealing with measurement errors originating from external lighting, occlusions or limitations in the measurement range is only rudimentary in literature. In this context we present a non-parametric extension to the ICP algorithm that is derived directly from measurement modalities of sensors in projective space. We show how aspects from reverse calibration can be embedded in search-tree-based approaches. Experiments demonstrate the applicability to range sensors like the Kinect device, Time-of-Flight cameras and 3D laser range finders. As a result the image registration becomes faster and more robust. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.3182/20120905-3-HR-2030.00057 | IFAC Proceedings Volumes |
Keywords | Field | DocType |
range image registration,localization,robot vision | Field of view,Computer vision,Robot localization,Artificial intelligence,Geography,Image registration,Observational error,Reflection mapping,Calibration,Iterative closest point,Projective space | Conference |
Volume | Issue | ISSN |
45 | 22 | 1474-6670 |
Citations | PageRank | References |
1 | 0.35 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Stefan May | 1 | 184 | 16.09 |
| Rainer Koch | 2 | 13 | 2.17 |
| Robert Scherlipp | 3 | 1 | 0.35 |
| Andreas Nüchter | 4 | 1341 | 90.03 |