  Autonomous locomotion for mobile ground robots in unstructured environments
such as waypoint navigation or flipper control requires a sufficiently accurate
prediction of the robot-terrain interaction. Heuristics like occupancy grids or
traversability maps are widely used but limit actions available to robots with
active flippers as joint positions are not taken into account. We present a
novel iterative geometric method to predict the 3D pose of mobile ground robots
with active flippers on uneven ground with high accuracy and online planning
capabilities. This is achieved by utilizing the ability of signed distance
fields to represent surfaces with sub-voxel accuracy. The effectiveness of the
presented approach is demonstrated on two different tracked robots in
simulation and on a real platform. Compared to a tracking system as ground
truth, our method predicts the robot position and orientation with an average
accuracy of 3.11 cm and 3.91{\deg}, outperforming a recent heightmap-based
approach. The implementation is made available as an open-source ROS package.
