27 citations · 51 across the 16 of their papers we have counts for
19 papers
Viking Hill Dataset: A Lidar-Radar-Camera Dataset for Detection and Segmentation in Forest Scenes
Vladimír Kubelka, Oleksandr Kotlyar, Unal Artan +1
Autonomous robots operating under forest canopies need robust perception of trees and surrounding vegetation across varying seasonal conditions. Existing forestry datasets provide…
Neural Surface and Reflectance Modelling from 3D Radar Data
Judith Treffler, Vladimír Kubelka, Henrik Andreasson +1
Robust scene representation is essential for autonomous systems to safely operate in challenging low-visibility environments. In these conditions, radar has a clear advantage over…
Introspective Loop Closure for SLAM with 4D Imaging Radar
Maximilian Hilger, Vladimír Kubelka, Daniel Adolfsson +3
Simultaneous Localization and Mapping (SLAM) allows mobile robots to navigate without external positioning systems or pre-existing maps. Radar is emerging as a valuable sensing too…
Towards introspective loop closure in 4D radar SLAM
Maximilian Hilger, Vladimír Kubelka, Daniel Adolfsson +2
Imaging radar is an emerging sensor modality in the context of Localization and Mapping (SLAM), especially suitable for vision-obstructed environments. This article investigates th…
4D Radar Semantic Segmentation of People in Field Conditions Using Temporal Multi-View Networks
Mikael Skog, Oleksandr Kotlyar, Vladimír Kubelka +1
Reliable people detection is crucial for the safe autonomy of mobile robots and heavy vehicles, both on roads and in industrial settings like mining and construction. However, comm…
Do we need scan-matching in radar odometry?
Vladimír Kubelka, Emil Fritz, Martin Magnusson
There is a current increase in the development of "4D" Doppler-capable radar and lidar range sensors that produce 3D point clouds where all points also have information about the r…
Doppler-only Single-scan 3D Vehicle Odometry
Andres Galeote-Luque, Vladimír Kubelka, Martin Magnusson +2
We present a novel 3D odometry method that recovers the full motion of a vehicle only from a Doppler-capable range sensor. It leverages the radial velocities measured from the scen…
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…
TBV Radar SLAM -- trust but verify loop candidates
Daniel Adolfsson, Mattias Karlsson, Vladimír Kubelka +2
Robust SLAM in large-scale environments requires fault resilience and awareness at multiple stages, from sensing and odometry estimation to loop closure. In this work, we present T…
Extrinsic calibration for highly accurate trajectories reconstruction
Maxime Vaidis, William Dubois, Alexandre Guénette +3
In the context of robotics, accurate ground-truth positioning is the cornerstone for the development of mapping and localization algorithms. In outdoor environments and over long d…
Gravity-constrained point cloud registration
Vladimír Kubelka, Maxime Vaidis, François Pomerleau
Visual and lidar Simultaneous Localization and Mapping (SLAM) algorithms benefit from the Inertial Measurement Unit (IMU) modality. The high-rate inertial data complement the other…
Kilometer-scale autonomous navigation in subarctic forests: challenges and lessons learned
Dominic Baril, Simon-Pierre Deschênes, Olivier Gamache +6
Challenges inherent to autonomous wintertime navigation in forests include lack of reliable a Global Navigation Satellite System (GNSS) signal, low feature contrast, high illuminat…
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…
Lidar Scan Registration Robust to Extreme Motions
Simon-Pierre Deschênes, Dominic Baril, Vladimír Kubelka +2
Registration algorithms, such as Iterative Closest Point (ICP), have proven effective in mobile robot localization algorithms over the last decades. However, they are susceptible t…
Accurate outdoor ground truth based on total stations
Maxime Vaidis, Philippe Giguère, François Pomerleau +1
In robotics, accurate ground-truth position fostered the development of mapping and localization algorithms through the creation of cornerstone datasets. In outdoor environments an…
Improving the Iterative Closest Point Algorithm using Lie Algebra
Maxime Vaidis, Johann Laconte, Vladimír Kubelka +1
Mapping algorithms that rely on registering point clouds inevitably suffer from local drift, both in localization and in the built map. Applications that require accurate maps, suc…
Evaluation of Skid-Steering Kinematic Models for Subarctic Environments
Dominic Baril, Vincent Grondin, Simon-Pierre Deschênes +6
In subarctic and arctic areas, large and heavy skid-steered robots are preferred for their robustness and ability to operate on difficult terrain. State estimation, motion control…
Predicting GNSS satellite visibility from dense point clouds
Philippe Dandurand, Philippe Babin, Vladimır Kubelka +2
To help future mobile agents plan their movement in harsh environments,a predictive model has been designed to determine what areas would be favorable for Global Navigation Satelli…
Large-scale 3D Mapping of Subarctic Forests
Philippe Babin, Philippe Dandurand, Vladimír Kubelka +2
The ability to map challenging subarctic environments opens new horizons for robotic deployments in industries such as forestry, surveillance, and open-pit mining. In this paper, w…