Static forces weighted Jacobian motion models for improved Odometry
In Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems, (IROS-2014), 14.9.-18.9.2014, Chicago, IEEE, Sep/2014.
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  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. Indepth error analysis and comparison with planar odometry is discussed, resulting in a more accurate localization.