Incremental Sparse GP Regression for Continuous-time Trajectory Estimation & Mapping

Abstract

Neural Information Processing Systems (NIPS) Workshop on Autonomously Learning Robots, December 2014.Recent work has investigated the problem of continuous-time trajectory estimation and mapping for mobile robots by formulating the problem as sparse Gaussian process regression. Gaussian processes provide a continuous-time representation of the robot trajectory, which elegantly handles asynchronous and sparse mea- surements, and allows the robot to query the trajectory to recover it’s estimated position at any time of interest. One of the major drawbacks of this approach is that Gaussian process regression formulates continuous-time trajectory estima- tion as a batch estimation problem. In this work, we provide the critical extensions necessary to transform this existing batch approach into an extremely efficient in- cremental approach. 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