Helen Oleynikova


Last Name

Oleynikova

First Name

Helen

ORCID

0000-0002-1831-2315

Organisational unit

09865 - Leutenegger, Stefan / Leutenegger, Stefan

Filters

2024 - 20256
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Publications 1 - 6 of 6
  • nvblox: GPU-Accelerated Incremental Signed Distance Field Mapping
    Item type: Conference Paper
    Millane, Alexander; Oleynikova, Helen; Wirbel, Emilie; et al. (2024)
    2024 IEEE International Conference on Robotics and Automation (ICRA)
    Dense, volumetric maps are essential to enable robot navigation and interaction with the environment. To achieve low latency, dense maps are typically computed onboard the robot, often on computationally constrained hardware. Previous works leave a gap between CPU-based systems for robotic mapping which, due to computation constraints, limit map resolution or scale, and GPU-based reconstruction systems which omit features that are critical to robotic path planning, such as computation of the Euclidean Signed Distance Field (ESDF). We introduce a library, nvblox, that aims to fill this gap, by GPU-accelerating robotic volumetric mapping. Nvblox delivers a significant performance improvement over the state of the art, achieving up to a 177× speed-up in surface reconstruction, and up to a 31× improvement in distance field computation, and is available open-source.
  • COIN-LIO: Complementary Intensity-Augmented LiDAR Inertial Odometry
    Item type: Conference Paper
    Pfreundschuh, Patrick; Oleynikova, Helen; Cadena, Cesar; et al. (2024)
    2024 IEEE International Conference on Robotics and Automation (ICRA)
    We present COIN-LIO, a LiDAR Inertial Odometry pipeline that tightly couples information from LiDAR intensity with geometry-based point cloud registration. The focus of our work is to improve the robustness of LiDAR-inertial odometry in geometrically degenerate scenarios, like tunnels or flat fields. We project LiDAR intensity returns into an image, and present a novel image processing pipeline that produces filtered images with improved brightness consistency within the image as well as across different scenes. We effectively leverage intensity as an additional modality, using our new feature selection scheme that detects uninformative directions in the point cloud registration and explicitly selects patches with complementary image information. Photometric error minimization in the image patches is then fused with inertial measurements and point-to-plane registration in an iterated Extended Kalman Filter. The proposed approach improves accuracy and robustness on a public dataset. We additionally publish a new dataset, that captures five real-world environments in challenging, geometrically degenerate scenes. By using the additional photometric information, our approach shows drastically improved robustness against geometric degeneracy in environments where all compared baseline approaches fail.
  • Learning to Fly Omnidirectional Micro Aerial Vehicles with an End-To-End Control Network
    Item type: Conference Paper
    Cuniato, Eugenio; Andersson, Olov; Oleynikova, Helen; et al. (2024)
    Springer Proceedings in Advanced Robotics ~ Experimental Robotics. ISER 2023
    Overactuated tilt-rotor platforms offer many advantages over traditional fixed-arm drones, allowing the decoupling of the applied force from the attitude of the robot. This expands their application areas to aerial interaction and manipulation, and allows them to overcome disturbances such as from ground or wall effects by exploiting the additional degrees of freedom available to their controllers. However, the overactuation also complicates the control problem, especially if the motors that tilt the arms have slower dynamics than those spinning the propellers. Instead of building a complex model-based controller that takes all of these subtleties into account, we attempt to learn an end-to-end pose controller using reinforcement learning, and show its superior behavior in the presence of inertial and force disturbances compared to a state-of-the-art traditional controller.
  • To Fuse or Not to Fuse: Measuring Consistency in Multi-Sensor Fusion for Aerial Robots
    Item type: Conference Paper
    Lanegger, Christian; Oleynikova, Helen; Pantic, Michael; et al. (2024)
    Springer Proceedings in Advanced Robotics ~ Experimental Robotics. ISER 2023
    Aerial vehicles are no longer limited to flying in open space: recent work has focused on aerial manipulation and up-close inspection. Such applications place stringent requirements on state estimation: the robot must combine state information from many sources, including onboard odometry and global positioning sensors. However, flying close to or in contact with structures is a degenerate case for many sensing modalities, and the robot's state estimation framework must intelligently choose which sensors are currently trustworthy. We evaluate a number of metrics to judge the reliability of sensing modalities in a multi-sensor fusion framework, then introduce a consensus-finding scheme that uses this metric to choose which sensors to fuse or not to fuse. Finally, we show that such a fusion framework is more robust and accurate than fusing all sensors all the time and demonstrate how such metrics can be informative in real-world experiments in indoor-outdoor flight and bridge inspection.
  • Pushing the Limits of Reactive Navigation: Learning to Escape Local Minima
    Item type: Journal Article
    Meijer, Isar; Pantic, Michael; Oleynikova, Helen; et al. (2025)
    IEEE Robotics and Automation Letters
    Can a robot navigate a cluttered environment without an explicit map? Reactive methods that use only the robot's current sensor data and local information are fast and flexible, but prone to getting stuck in local minima. Is there a middle-ground between reactive methods and map-based path planners? In this paper, we investigate feed forward and recurrent networks to augment a purely reactive sensor-based navigation algorithm, which should give the robot "geometric intuition" about how to escape local minima. We train on a large number of extremely cluttered simulated worlds, auto-generated from primitive shapes, and show that our system zero-shot transfers to worlds based on real data 3D man-made environments, and can handle up to 30% sensor noise without degradation of performance. We also offer a discussion of what role network memory plays in our final system, and what insights can be drawn about the nature of reactive vs. map-based navigation.
  • Robotics-enabled roadwork maintenance and upgrading
    Item type: Book Chapter
    Bakalos, Nikolaos; Katsamenis, Iason; Protopapadakis, Eftychios; et al. (2024)
    Robotics and Automation Solutions for Inspection and Maintenance in Critical Infrastructures
    Closing, this chapter presents recent research efforts and results on a Robotics-enabled Roadwork Maintenance and Upgrading approach and tools. This includes the implementation of a road infrastructure blueprint developed including advanced engineering solutions for interconnecting and facilitating seamless transitions between different transportation modes in the event of severe disruptions affecting one mode of transportation.
Publications 1 - 6 of 6