Incremental Sparse GP Regression for Continuous-time Trajectory Estimation & Mapping
This incremental approach increases the practicality of Gaussian process methods for robot mapping and localization, addressing a specific bottleneck in mobile robotics.
The paper tackled the batch estimation limitation in simultaneous trajectory estimation and mapping (STEAM) by developing an incremental algorithm that uses efficient variable reordering and sparse updates, resulting in vastly sped-up solution times demonstrated on synthetic and real datasets.
Recent work on simultaneous trajectory estimation and mapping (STEAM) for mobile robots has found success by representing the trajectory as a Gaussian process. Gaussian processes can represent a continuous-time trajectory, elegantly handle asynchronous and sparse measurements, and allow the robot to query the trajectory to recover its estimated position at any time of interest. A major drawback of this approach is that STEAM is formulated as a batch estimation problem. In this paper we provide the critical extensions necessary to transform the existing batch algorithm into an extremely efficient incremental algorithm. In particular, we are able to vastly speed up the solution time through efficient variable reordering and incremental sparse updates, which we believe will greatly increase the practicality of Gaussian process methods for robot mapping and localization. Finally, we demonstrate the approach and its advantages on both synthetic and real datasets.