Louis Wiesmann

Orcid: 0000-0003-0985-7433

According to our database¹, Louis Wiesmann authored at least 22 papers between 2021 and 2024.

Collaborative distances:

Dijkstra number² of five.
Erdős number³ of four.

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Bibliography

2024

Joint Intrinsic and Extrinsic Calibration of Perception Systems Utilizing a Calibration Environment.

[BibT_eX]

[DOI]

IEEE Robotics Autom. Lett., October, 2024

SPR: Single-Scan Radar Place Recognition.

[BibT_eX]

[DOI]

Daniel Casado Herraez

IEEE Robotics Autom. Lett., October, 2024

PIN-SLAM: LiDAR SLAM Using a Point-Based Implicit Neural Representation for Achieving Global Map Consistency.

[BibT_eX]

[DOI]

IEEE Trans. Robotics, 2024

Efficient LiDAR Bundle Adjustment for Multi-Scan Alignment Utilizing Continuous-Time Trajectories.

[BibT_eX]

[DOI]

CoRR, 2024

LIO-EKF: High Frequency LiDAR-Inertial Odometry using Extended Kalman Filters.

[BibT_eX]

[DOI]

Proceedings of the IEEE International Conference on Robotics and Automation, 2024

2023

Mask4D: End-to-End Mask-Based 4D Panoptic Segmentation for LiDAR Sequences.

[BibT_eX]

[DOI]

IEEE Robotics Autom. Lett., November, 2023

Static map generation from 3D LiDAR point clouds exploiting ground segmentation.

[BibT_eX]

[DOI]

Robotics Auton. Syst., 2023

KPPR: Exploiting Momentum Contrast for Point Cloud-Based Place Recognition.

[BibT_eX]

[DOI]

IEEE Robotics Autom. Lett., 2023

LocNDF: Neural Distance Field Mapping for Robot Localization.

[BibT_eX]

[DOI]

IEEE Robotics Autom. Lett., 2023

KISS-ICP: In Defense of Point-to-Point ICP - Simple, Accurate, and Robust Registration If Done the Right Way.

[BibT_eX]

[DOI]

IEEE Robotics Autom. Lett., 2023

High Precision Leaf Instance Segmentation for Phenotyping in Point Clouds Obtained Under Real Field Conditions.

[BibT_eX]

[DOI]

IEEE Robotics Autom. Lett., 2023

Mask-Based Panoptic LiDAR Segmentation for Autonomous Driving.

[BibT_eX]

[DOI]

IEEE Robotics Autom. Lett., 2023

Toward Reproducible Version-Controlled Perception Platforms: Embracing Simplicity in Autonomous Vehicle Dataset Acquisition.

[BibT_eX]

[DOI]

Proceedings of the 25th IEEE International Conference on Intelligent Transportation Systems, 2023

Temporal Consistent 3D LiDAR Representation Learning for Semantic Perception in Autonomous Driving.

[BibT_eX]

[DOI]

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023

2022

DCPCR: Deep Compressed Point Cloud Registration in Large-Scale Outdoor Environments.

[BibT_eX]

[DOI]

IEEE Robotics Autom. Lett., 2022

Make it Dense: Self-Supervised Geometric Scan Completion of Sparse 3D LiDAR Scans in Large Outdoor Environments.

[BibT_eX]

[DOI]

IEEE Robotics Autom. Lett., 2022

Contrastive Instance Association for 4D Panoptic Segmentation Using Sequences of 3D LiDAR Scans.

[BibT_eX]

[DOI]

IEEE Robotics Autom. Lett., 2022

Robust Onboard Localization in Changing Environments Exploiting Text Spotting.

[BibT_eX]

[DOI]

Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, 2022

Retriever: Point Cloud Retrieval in Compressed 3D Maps.

[BibT_eX]

[DOI]

Proceedings of the 2022 International Conference on Robotics and Automation, 2022

2021

Deep Compression for Dense Point Cloud Maps.

[BibT_eX]

[DOI]

IEEE Robotics Autom. Lett., 2021

Moving Object Segmentation in 3D LiDAR Data: A Learning-Based Approach Exploiting Sequential Data.

[BibT_eX]

[DOI]

IEEE Robotics Autom. Lett., 2021

Mapping the Static Parts of Dynamic Scenes from 3D LiDAR Point Clouds Exploiting Ground Segmentation.

[BibT_eX]

[DOI]

Proceedings of the 10th European Conference on Mobile Robots, 2021

Louis Wiesmann

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