Benedikt Mersch
Orcid: 0000-0002-6937-2799
According to our database1,
Benedikt Mersch
authored at least 19 papers
between 2021 and 2025.
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Bibliography
2025
CoRR, January, 2025
2024
Radar Instance Transformer: Reliable Moving Instance Segmentation in Sparse Radar Point Clouds.
IEEE Trans. Robotics, 2024
Generalizable Stable Points Segmentation for 3D LiDAR Scan-to-Map Long-Term Localization.
IEEE Robotics Autom. Lett., 2024
Kinematic-ICP: Enhancing LiDAR Odometry with Kinematic Constraints for Wheeled Mobile Robots Moving on Planar Surfaces.
CoRR, 2024
HeLiMOS: A Dataset for Moving Object Segmentation in 3D Point Clouds From Heterogeneous LiDAR Sensors.
Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, 2024
Proceedings of the IEEE International Conference on Robotics and Automation, 2024
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024
2023
KISS-ICP: In Defense of Point-to-Point ICP - Simple, Accurate, and Robust Registration If Done the Right Way.
IEEE Robotics Autom. Lett., 2023
Building Volumetric Beliefs for Dynamic Environments Exploiting Map-Based Moving Object Segmentation.
IEEE Robotics Autom. Lett., 2023
Proceedings of the Robotics: Science and Systems XIX, Daegu, 2023
Toward Reproducible Version-Controlled Perception Platforms: Embracing Simplicity in Autonomous Vehicle Dataset Acquisition.
Proceedings of the 25th IEEE International Conference on Intelligent Transportation Systems, 2023
Radar Velocity Transformer: Single-scan Moving Object Segmentation in Noisy Radar Point Clouds.
Proceedings of the IEEE International Conference on Robotics and Automation, 2023
2022
Make it Dense: Self-Supervised Geometric Scan Completion of Sparse 3D LiDAR Scans in Large Outdoor Environments.
IEEE Robotics Autom. Lett., 2022
IEEE Robotics Autom. Lett., 2022
Automatic Labeling to Generate Training Data for Online LiDAR-Based Moving Object Segmentation.
IEEE Robotics Autom. Lett., 2022
2021
Moving Object Segmentation in 3D LiDAR Data: A Learning-Based Approach Exploiting Sequential Data.
IEEE Robotics Autom. Lett., 2021
Maneuver-based Trajectory Prediction for Self-driving Cars Using Spatio-temporal Convolutional Networks.
Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, 2021
Embedded Stochastic Field Exploration with Micro Diving Agents using Bayesian Optimization-Guided Tree-Search and GMRFs.
Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, 2021
Self-supervised Point Cloud Prediction Using 3D Spatio-temporal Convolutional Networks.
Proceedings of the Conference on Robot Learning, 8-11 November 2021, London, UK., 2021