Name
Abstract | ||
|---|---|---|
Grid map registration is an important field in mobile robotics. Applications in which multiple robots are involved benefit from multiple aligned grid maps as they provide an efficient exploration of the environment in parallel. In this paper, a normal distribution transform (NDT)-based approach for grid map registration is presented. For simultaneous mapping and localization approaches on laser data, the NDT is widely used to align new laser scans to reference scans. The original grid quantization-based NDT results in good registration performances but has poor convergence properties due to discontinuities of the optimization function and absolute grid resolution. This paper shows that clustering techniques overcome disadvantages of the original NDT by significantly improving the convergence basin for aligning grid maps. A multi-scale clustering method results in an improved registration performance which is shown on real world experiments on radar data. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/IVS.2015.7225694 | 2015 IEEE Intelligent Vehicles Symposium (IV) |
Keywords | Field | DocType |
normal distribution transform,grid map registration,grid quantization-based NDT,optimization function,multiscale clustering method,simultaneous localization and mapping,SLAM | Convergence (routing),Radar,Computer vision,Grid reference,Computer science,Nondestructive testing,Artificial intelligence,Quantization (signal processing),Cluster analysis,Robot,Grid | Conference |
ISSN | Citations | PageRank |
1931-0587 | 3 | 0.41 |
References | Authors | |
13 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Matthias Rapp | 1 | 3 | 0.41 |
| Michael Barjenbruch | 2 | 26 | 3.64 |
| markus hahn | 3 | 17 | 3.48 |
| Jürgen Dickmann | 4 | 83 | 14.07 |
| Klaus Dietmayer | 5 | 822 | 102.64 |