Name
Abstract | ||
|---|---|---|
This paper presents a real time monocular EKF SLAM process that uses only Cartesian defined landmarks. This representation is easy to handle, light and consequently fast. However, it is prone to linearization errors which can cause the filter to diverge. Here, we will first clearly identify and explain when those problems take place. Then, a solution, able to reduce or avoid the errors involved by the linearization process, will be proposed. Combined with an EKF, our method uses resources parsimoniously by conserving landmarks for a long period of time without requiring many points to be efficient. Our solution is based on a method to properly compute the projection of a 3D uncertainty into the image frame in order to track landmarks efficiently. The second part of this solution relies on a correction of the Kalman gain that reduces the impact of the update when it is incoherent. This approach was applied to a real data set presenting difficult conditions such as severe distortions, reflections, blur or sunshine to illustrate its robustness. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/IVS.2012.6232203 | 2012 IEEE INTELLIGENT VEHICLES SYMPOSIUM (IV) |
Keywords | Field | DocType |
sunshine,robustness,kalman filters,simultaneous localization and mapping,reflection,vectors,object tracking,uncertainty | Computer vision,Extended Kalman filter,Robustness (computer science),Kalman filter,Video tracking,Artificial intelligence,Simultaneous localization and mapping,Monocular,Mathematics,Linearization,Cartesian coordinate system | Conference |
Citations | PageRank | References |
3 | 0.39 | 14 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Guillaume Bresson | 1 | 20 | 5.83 |
| Thomas Féraud | 2 | 17 | 2.26 |
| Romuald Aufrère | 3 | 71 | 11.46 |
| Paul Checchin | 4 | 106 | 14.70 |
| Roland Chapuis | 5 | 299 | 42.01 |