33 citations · 124 across the 13 of their papers we have counts for
14 papers
Manual, Semi or Fully Autonomous Flipper Control? A Framework for Fair Comparison
Valentýn Číhala, Martin Pecka, Tomáš Svoboda +1
We investigated the performance of existing semi- and fully autonomous methods for controlling flipper-based skid-steer robots. Our study involves reimplementation of these methods…
Let-It-Flow: Simultaneous Optimization of 3D Flow and Object Clustering
Patrik Vacek, David Hurych, Tomáš Svoboda +1
We study the problem of self-supervised 3D scene flow estimation from real large-scale raw point cloud sequences, which is crucial to various tasks like trajectory prediction or in…
Regularizing Self-supervised 3D Scene Flows with Surface Awareness and Cyclic Consistency
Patrik Vacek, David Hurych, Karel Zimmermann +2
Learning without supervision how to predict 3D scene flows from point clouds is essential to many perception systems. We propose a novel learning framework for this task which impr…
T-UDA: Temporal Unsupervised Domain Adaptation in Sequential Point Clouds
Awet Haileslassie Gebrehiwot, David Hurych, Karel Zimmermann +2
Deep perception models have to reliably cope with an open-world setting of domain shifts induced by different geographic regions, sensor properties, mounting positions, and several…
MonoForce: Self-supervised Learning of Physics-informed Model for Predicting Robot-terrain Interaction
Ruslan Agishev, Karel Zimmermann, Vladimír 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…
Real3D-Aug: Point Cloud Augmentation by Placing Real Objects with Occlusion Handling for 3D Detection and Segmentation
Petr Šebek, Šimon Pokorný, Patrik Vacek +1
Object detection and semantic segmentation with the 3D lidar point cloud data require expensive annotation. We propose a data augmentation method that takes advantage of already an…
Teachers in concordance for pseudo-labeling of 3D sequential data
Awet Haileslassie Gebrehiwot, Patrik Vacek, David Hurych +3
Automatic pseudo-labeling is a powerful tool to tap into large amounts of sequential unlabeled data. It is specially appealing in safety-critical applications of autonomous driving…
UAVs Beneath the Surface: Cooperative Autonomy for Subterranean Search and Rescue in DARPA SubT
Matej Petrlik, Pavel Petracek, Vit Kratky +8
This paper presents a novel approach for autonomous cooperating UAVs in search and rescue operations in subterranean domains with complex topology. The proposed system was ranked s…
System for multi-robotic exploration of underground environments CTU-CRAS-NORLAB in the DARPA Subterranean Challenge
Tomáš Rouček, Martin Pecka, Petr Čížek +19
We present a field report of CTU-CRAS-NORLAB team from the Subterranean Challenge (SubT) organised by the Defense Advanced Research Projects Agency (DARPA). The contest seeks to ad…
PreCNet: Next-Frame Video Prediction Based on Predictive Coding
Zdenek Straka, Tomas Svoboda, Matej Hoffmann
Predictive coding, currently a highly influential theory in neuroscience, has not been widely adopted in machine learning yet. In this work, we transform the seminal model of Rao a…
Data-driven Policy Transfer with Imprecise Perception Simulation
Martin Pecka, Karel Zimmermann, Matěj Petrlík +1
The paper presents a complete pipeline for learning continuous motion control policies for a mobile robot when only a non-differentiable physics simulator of robot-terrain interact…
Learning for Active 3D Mapping
Karel Zimmermann, Tomas Petricek, Vojtech Salansky +1
We propose an active 3D mapping method for depth sensors, which allow individual control of depth-measuring rays, such as the newly emerging solid-state lidars. The method simultan…
Fast Simulation of Vehicles with Non-deformable Tracks
Martin Pecka, Karel Zimmermann, Tomáš Svoboda
This paper presents a novel technique that allows for both computationally fast and sufficiently plausible simulation of vehicles with non-deformable tracks. The method is based on…
Controlling Robot Morphology from Incomplete Measurements
Martin Pecka, Karel Zimmermann, Michal Reinštein +1
Mobile robots with complex morphology are essential for traversing rough terrains in Urban Search & Rescue missions (USAR). Since teleoperation of the complex morphology causes hig…