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Ruslan Agishev

3 accepted papers

2024

MonoForce: Self-supervised Learning of Physics-informed Model for Predicting Robot-terrain Interaction

IROS 2024poster

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 address it, we introduce an explainable, physics-aware and end-to-end differentiable model which predicts the outcome of rob…

Cited by 1SourcecodeScholar
2023

Self-Supervised Depth Correction of Lidar Measurements From Map Consistency Loss

RA-L 2023

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-level <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">light detection

Cited by 3SourcecodeScholar
2022

Trajectory Optimization Using Learned Robot-Terrain Interaction Model in Exploration of Large Subterranean Environments

RA-L 2022

We consider the task of active exploration of large subterranean environments with a ground mobile robot. Our goal is to autonomously explore a large unknown area and to obtain an accurate coverage and localization of objects of interest (artifacts). The exploration is constrained by the restricted

Cited by 11SourceScholar