4 papers
ProTerrain: Probabilistic Physics-Informed Rough Terrain World Modeling
Golnaz Raja, Ruslan Agishev, Miloš Prágr +4
Uncertainty-aware robot motion prediction is crucial for downstream traversability estimation and safe autonomous navigation in unstructured, off-road environments, where terrain i…
MonoForce: Self-supervised Learning of Physics-informed Model for Predicting Robot-terrain Interaction
Ruslan Agishev, Karel Zimmermann, VladimÃr Kubelka +2
While autonomous navigation of mobile robots on rigid terrain is a well-explored problem, navigating on deformable terrain such as tall grass or bushes remains a challenge. To addr…
FusionForce: End-to-end Differentiable Neural-Symbolic Layer for Trajectory Prediction
Ruslan Agishev, Karel Zimmermann
We propose end-to-end differentiable model that predicts robot trajectories on rough offroad terrain from camera images and/or lidar point clouds. The model integrates a learnable…
Self-Supervised Depth Correction of Lidar Measurements from Map Consistency Loss
Ruslan Agishev, Tomáš PÄtÅÃÄek, Karel Zimmermann
Depth perception is considered an invaluable source of information in the context of 3D mapping and various robotics applications. However, point cloud maps acquired using consumer…