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-# RMS
-Code for RA-L paper "RMS: Redundancy-Minimizing Point Cloud Sampling for Real-Time Pose Estimation in Degenerated Environments"
+# RMS: Redundancy-Minimizing Point Cloud Sampling
+
+[![RMS](./fig/snapshot.jpg)](https://www.youtube.com/watch?v=Y9ZlRrX1UBY)
+
+  * novel method for sampling large 3D LiDAR point clouds
+  * replacement for voxelization
+    * pipelines using **RMS** are faster (lower latency) and more accurate (less drift)
+  * designed for **real-time LiDAR-based** 6-DoF odometry/SLAM pipelines
+    * both point-based (ICP-like) and feature-based (LOAM-like) methods 
+    * potentially improving most L, LI, LVI, LV pipelines  
+  * **single parameter only -> depends on the SLAM pipeline (and not the environment!)**
+    * tuned just once given your pipeline
+  * deterministic (no data for learning needed) 
+
+#### Code & How to
+The code will be made available upon acceptance.
+
+#### Paper
+Submitted to IEEE RA-L on December 1, 2023.
+Preprint available at arXiv: TODO.
+
+#### How to cite
+```
+@article{petracek2023rms,
+  title   = {{RMS: Redundancy-Minimizing Point Cloud Sampling for Real-Time Pose Estimation in Degenerated Environments}}, 
+  author  = {Petracek, Pavel and Alexis, Kostas and Saska, Martin}, 
+  year    = {2023},
+  journal = {arXiv:TODO},
+  note    = {Submitted to IEEE RA-L on December 1, 2023}
+}
+```
+
+#### Acknowledgment
+To be added upon acceptance.
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