Name
Abstract | ||
|---|---|---|
Visual inertial odometry (VIO) is a technique to estimate the change of a mobile platform in position and orientation overtime using the measurements from on-board cameras and IMU sensor. Recently, VIO attracts significant attentions from large number of researchers and is gaining the popularity in various potential applications due to the miniaturisation in size and low cost in price of two sensing modularities. However, it is very challenging in both of technical development and engineering implementation when accuracy, real-time performance, robustness and operation scale are taken into consideration. This survey is to report the state of the art VIO techniques from the perspectives of filtering and optimisation-based approaches, which are two dominated approaches adopted in the research area. To do so, various representations of 3D rigid motion body are illustrated. Then filtering-based approaches are reviewed, and followed by optimisation-based approaches. The links between these two approaches will be clarified via a framework of the Bayesian Maximum A Posterior. Other features, such as observability and self calibration, will be discussed. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1080/01691864.2015.1057616 | ADVANCED ROBOTICS |
Keywords | Field | DocType |
visual inertial odometry,SLAM,Kalman filtering,state estimation | Inertial frame of reference,Computer vision,Rigid motion,Visual odometry,Odometry,Filter (signal processing),Kalman filter,Control engineering,Robustness (computer science),Artificial intelligence,Inertial measurement unit,Engineering | Journal |
Volume | Issue | ISSN |
29 | 20 | 0169-1864 |
Citations | PageRank | References |
6 | 0.47 | 28 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jianjun Gui | 1 | 16 | 1.95 |
| Dongbing Gu | 2 | 769 | 72.81 |
| Sen Wang | 3 | 279 | 21.15 |
| Huosheng Hu | 4 | 2009 | 220.95 |