In State-of-the-Art decentralized mapping methods, optimization (correcting odometry drift) is typically done using pose graph optimization due to the fact that a pose graph is a very compact representation. Unfortunately, this compression in data results in limitations in precision and robustness. Bundle Adjustment is a map optimization method for visual maps which is much more precise and robust, but also much more data intensive.
In State-of-the-Art decentralized mapping methods, optimization (correcting odometry drift) is typically done using pose graph optimization due to the fact that a pose graph is a very compact representation. Unfortunately, this compression in data results in limitations in precision and robustness. Bundle Adjustment is a map optimization method for visual maps which is much more precise and robust, but also much more data intensive.
In this work, you will figure out a way to achieve the superior precision of Bundle Adjustment while minimizing the amount of data that needs to be exchanged between robots in a decentralized setting.
In this work, you will figure out a way to achieve the superior precision of Bundle Adjustment while minimizing the amount of data that needs to be exchanged between robots in a decentralized setting.
Titus Cieslewski ( titus at ifi.uzh.ch ), APPLY VIA EMAIL, ATTACH CV AND TRANSCRIPT! Required skills: Matlab or C++, with a preference for the latter. Desirable: Background in optimization (Nonlinear least squares, Gauss-Newton or similar)
Titus Cieslewski ( titus at ifi.uzh.ch ), APPLY VIA EMAIL, ATTACH CV AND TRANSCRIPT! Required skills: Matlab or C++, with a preference for the latter. Desirable: Background in optimization (Nonlinear least squares, Gauss-Newton or similar)