Name
Abstract | ||
|---|---|---|
We present a solution for constant-time self-calibration and change detection of multiple sensor intrinsic and extrinsic calibration parameters without any prior knowledge of the initial system state or the need of a calibration target or special initialization sequence. This system is capable of continuously self-calibrating multiple sensors in an online setting, while seamlessly solving the online SLAM problem in real-time. We focus on the camera-IMU extrinsic calibration, essential for accurate long-term vision-aided inertial navigation. An initialization strategy and method for continuously estimating and detecting changes to the maximum likelihood camera-IMU transform are presented. A conditioning approach is used, avoiding problems associated with early linearization. Experimental data is presented to evaluate the proposed system and compare it with artifact-based offline calibration developed by our group. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1007/978-3-319-50115-4_66 | Springer Proceedings in Advanced Robotics |
Keywords | Field | DocType |
Self-calibration,SLAM,Constant-time,Change detection | Inertial navigation system,Computer vision,Change detection,Experimental data,Maximum likelihood,Control engineering,Artificial intelligence,Initialization,Engineering,Multiple sensors,Linearization,Calibration | Conference |
Volume | ISSN | Citations |
1 | 2511-1256 | 0 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Fernando Nobre | 1 | 3 | 1.40 |
| Christoffer R. Heckman | 2 | 12 | 10.78 |
| Gabe Sibley | 3 | 710 | 49.50 |