Name
Abstract | ||
|---|---|---|
It is known that in the simultaneous localization and mapping (SLAM) problem when a robot's orientation is known, an estimation of the history of its poses can be formulated as a standard linear least squares problem. In this letter, we exploit this property of SLAM to develop a robust pose-graph SLAM framework that uses absolute orientation sensing. Our contribution are as follows: 1) we show that absolute orientation can be estimated using local structural cues; and 2) we develop a method to incorporate absolute orientation measurements in both the front and back-end of pose-graph SLAM. We also demonstrate our approach through extensive simulations and a physical real-world demonstration along with comparisons against existing state-of-the-art solvers that do not use absolute orientation. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/LRA.2019.2893436 | IEEE ROBOTICS AND AUTOMATION LETTERS |
Keywords | Field | DocType |
SLAM, localization, mapping, range sensing, visual-based navigation | Graph,Control theory,Algorithm,Absolute orientation,Engineering,Simultaneous localization and mapping,Robot,Linear least squares | Journal |
Volume | Issue | ISSN |
4 | 2 | 2377-3766 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| saurav agarwal | 1 | 8 | 2.56 |
| Karthikeya S. Parunandi | 2 | 0 | 1.69 |
| S. Chakravorty | 3 | 127 | 25.20 |