Name
Abstract | ||
|---|---|---|
We present a solution for online simultaneous localization and mapping (SLAM) self-calibration in the presence of drift in calibration parameters in order to support accurate long-term operation. Calibration parameters such as the camera focal length or camera-to-IMU extrinsics are frequently subject to drift over long periods of operation, inducing cumulative error in the reconstruction. The key contributions are modeling calibration parameters as a spatiotemporal quantity: sensor-to-sensor spatial calibration and sensor intrinsic parameters are continuously time-varying, with statistical tests for change detection and regression. An analysis of the long term effects of inappropriately modeling time-varying sensor calibration is also provided. Constant-time operation is achieved by selecting only a fixed number of informative segments of the trajectory for calibration parameter estimation, giving the added benefit of avoiding early linearization errors by not rolling past measurements into a prior distribution. Our approach is validated with simulated and real-world data. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/ICRA.2017.7989771 | ICRA |
Field | DocType | Volume |
Computer vision,Change detection,Robot calibration,Measurement uncertainty,Artificial intelligence,Engineering,Estimation theory,Simultaneous localization and mapping,Linearization,Calibration,Trajectory | Conference | 2017 |
Issue | Citations | PageRank |
1 | 1 | 0.35 |
References | Authors | |
16 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Fernando Nobre | 1 | 3 | 1.40 |
| Michael Kasper | 2 | 1 | 0.35 |
| Christoffer R. Heckman | 3 | 12 | 10.78 |