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.

The estimation of robot's motion at the prediction step of any localization framework is commonly performed using a motion model in conjunction with inertial measurements. In the context of field robotics, articulated mobile robots have complex chassis. They might require a complete model in comparison with the traditionally used planar assumption. In this paper, we use a Jacobian motion model-based approach for real-time inertial-aided odometry. The work makes use of the transformation approach [1] to accurately model 6-DoF kinematics. The algorithm relates normal forces with the probability of a contact-point to slip. The result increases the accuracy by weighting the least-squares solution using static forces prediction. The method is applied to the Asguard v3 system, a simple but highly capable leg-wheel hybrid robot. The performance of the approach is demonstrated in extensive field testing within different unstructured environments. In-depth error analysis and comparison with planar odometry is discussed, resulting in a more accurate localization.