Name
Abstract | ||
|---|---|---|
This paper presents a linear approach for solving monocular simultaneous localization and mapping (SLAM) problems. The algorithm first builds a sequence of small initial submaps and then joins these submaps together in a divide-and-conquer (D&C) manner. Each of the initial submap is built using three monocular images by bundle adjustment (BA), which is a simple nonlinear optimization problem. Each step in the D&C submap joining is solved by a linear least squares together with a coordinate and scale transformation. Since the only nonlinear part is in the building of the initial submaps, the algorithm makes it possible to solve large-scale monocular SLAM while avoiding issues associated with initialization, iteration, and local minima that are present in most of the nonlinear optimization based algorithms currently used for large-scale monocular SLAM. Experimental results based on publically available datasets are used to demonstrate that the proposed algorithms yields solutions that are very close to those obtained using global BA starting from good initial guess. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/ICRA.2014.6907053 | ICRA |
Keywords | Field | DocType |
bundle adjustment,d&c submap,nonlinear programming,least squares approximations,structure-from-motion problem,large-scale monocular simultaneous localization-and-mapping problems,nonlinear optimization problem,slam (robots),divide-and-conquer,linear least squares,linear monoslam,divide and conquer methods | Computer vision,Nonlinear system,Bundle adjustment,Nonlinear programming,Maxima and minima,Artificial intelligence,Initialization,Monocular,Simultaneous localization and mapping,Linear least squares,Mathematics | Conference |
Volume | Issue | ISSN |
2014 | 1 | 1050-4729 |
Citations | PageRank | References |
2 | 0.36 | 20 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Liang Zhao | 1 | 100 | 13.74 |
| Shoudong Huang | 2 | 755 | 62.77 |
| Gamini Dissanayake | 3 | 2226 | 256.36 |