Louis Wiesmann
Papers
14
Total Citations
1,033
H-Index
12
About
Louis Wiesmann is a robotics researcher whose work centers on 3D LiDAR perception, simultaneous localization and mapping (SLAM), and autonomous navigation. He has made significant contributions to the field of LiDAR-based odometry and scene understanding, most notably through KISS-ICP (2023), a remarkably elegant point-to-point registration framework that challenged the prevailing trend of ever-increasing algorithmic complexity — demonstrating that simplicity, when thoughtfully designed, can outperform sophisticated pipelines. This work has garnered over 435 citations and has been widely adopted in the robotics community. His research on moving object segmentation in 3D LiDAR data (237 citations) introduced a learning-based sequential approach that significantly advanced the ability of robots to perceive dynamic environments. Wiesmann has also pushed boundaries in neural implicit representations through PIN-SLAM, which achieves globally consistent mapping using point-based implicit neural fields. His broader portfolio spans point cloud compression, scan completion, LiDAR-inertial odometry, and robust localization in changing environments. With nearly 1,000 cumulative citations across ten papers, Wiesmann has established himself as a prolific and influential voice in modern mobile robotics research.
Research Focus
Key Achievements
Top Papers
- 1
- 2
- 3Deep Compression for Dense Point Cloud Maps99 citations · 2021
- 4
- 5LocNDF: Neural Distance Field Mapping for Robot Localization31 citations · 2023
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- 7Retriever: Point Cloud Retrieval in Compressed 3D Maps26 citations · 2022
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- 10