# Registration of 3D Point Sets Using Correntropy Similarity Matrix

arXiv: https://arxiv.org/abs/2107.09725 · HTML: https://mlsanity.com/paper/2107.09725/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 24 references that are in the map.
- First posted on arXiv 20 Jul 2021.

## Builds on (most cited first, 6 of 6)

- [Past, Present, and Future of Simultaneous Localization And Mapping: Towards the Robust-Perception Age](https://mlsanity.com/paper/1606.05830/) (2016)
- [TEASER: Fast and Certifiable Point Cloud Registration](https://mlsanity.com/paper/2001.07715/) (2020)
- [Deep Global Registration](https://mlsanity.com/paper/2004.11540/) (2020)
- [CNN-SLAM: Real-time dense monocular SLAM with learned depth prediction](https://mlsanity.com/paper/1704.03489/) (2017)
- [Lidar-Monocular Visual Odometry with Genetic Algorithm for Parameter Optimization](https://mlsanity.com/paper/1903.02046/) (2019)
- [Learning a Bias Correction for Lidar-only Motion Estimation](https://mlsanity.com/paper/1801.04678/) (2018)

## Built on by (most cited first, 1 of 1)

- [DeepMapping2: Self-Supervised Large-Scale LiDAR Map Optimization](https://mlsanity.com/paper/2212.06331/) (2022)

## Abstract (arXiv)

This work focuses on Registration or Alignment of 3D point sets. Although the Registration problem is a well established problem and it's solved using multiple variants of Iterative Closest Point (ICP) Algorithm, most of the approaches in the current state of the art still suffers from misalignment when the textitSource and the textitTarget point sets are separated by large rotations and translation. In this work, we propose a variant of the Standard ICP algorithm, where we introduce a Correntropy Relationship Matrix in the computation of rotation and translation component which attempts to solve the large rotation and translation problem between textitSource and textitTarget point sets. This matrix is created through correntropy criterion which is updated in every iteration. The correntropy criterion defined in this approach maintains the relationship between the points in the textitSource dataset and the textitTarget dataset. Through our experiments and validation we verify that our approach has performed well under various rotation and translation in comparison to the other well-known state of the art methods available in the Point Cloud Library (PCL) as well as other methods available as open source. We have uploaded our code in the github repository for the readers to validate and verify our approach https://github.com/aralab-unr/CoSM-ICP.