Name
Abstract | ||
|---|---|---|
In this paper, a sliding-window two-camera vision-aided inertial navigation system (VINS) is presented in the square-root inverse domain. The performance of the system is assessed for the cases where feature matches across the two-camera images are processed with or without any stereo constraints (i.e., stereo vs. binocular). To support the comparison results, a theoretical analysis on the information gain when transitioning from binocular to stereo is also presented. Additionally, the advantage of using a two-camera (both stereo and binocular) system over a monocular VINS is assessed. Furthermore, the impact on the achieved accuracy of different image-processing frontends and estimator design choices is quantified. Finally, a thorough evaluation of the algorithm's processing requirements, which runs in real-time on a mobile processor, as well as its achieved accuracy as compared to alternative approaches is provided, for various scenes and motion profiles. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/ICRA.2017.7989022 | 2017 IEEE International Conference on Robotics and Automation (ICRA) |
Keywords | Field | DocType |
sliding-window two-camera vision aided inertial navigation system,square-root inverse domain,two-camera image processing,tightly-coupled monocular binocular stereo VINS,mobile processor | Inertial navigation system,Computer vision,Noise measurement,Visualization,Mobile processor,Feature extraction,Artificial intelligence,Engineering,Simultaneous localization and mapping,Monocular,Computer stereo vision | Conference |
Volume | Issue | ISBN |
2017 | 1 | 978-1-5090-4634-8 |
Citations | PageRank | References |
4 | 0.53 | 12 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mrinal K. Paul | 1 | 4 | 0.53 |
| Kejian Wu | 2 | 6 | 1.23 |
| Joel A. Hesch | 3 | 273 | 13.62 |
| Esha D. Nerurkar | 4 | 144 | 8.58 |
| Stergios I. Roumeliotis | 5 | 2124 | 151.96 |