Gaussian Process Estimation of Odometry Errors for Localization and Mapping
In IEEE International Conference on Robotics and Automation, (ICRA), 29.5.-03.6.2017, Singapore, IEEE, 2017.
Since early in robotics the performance of
odometry techniques has been of constant research for mobile
robots. This is due to its direct influence on localization. The
pose error grows unbounded in dead-reckoning systems and its
uncertainty has negative impacts in localization and mapping
(i.e. SLAM). The dead-reckoning performance in terms of
residuals, i.e. the difference between the expected and the real
pose state, is related to the statistical error or uncertainty
in probabilistic motion models. A novel approach to model
odometry errors using Gaussian processes (GPs) is presented.
The methodology trains a GP on the residual between the
non-linear parametric motion model and the ground truth
training data. The result is a GP over odometry residuals
which provides an expected value and its uncertainty in order
to enhance the belief with respect to the parametric model.
The localization and mapping benefits from a comprehensive
GP-odometry residuals model. The approach is applied to a
planetary rover in an unstructured environment. We show that
our approach enhances visual SLAM by efficiently computing
image frames and effectively distributing keyframes
machine learning,slam,space robotics