# CLEAR: A Consistent Lifting, Embedding, and Alignment Rectification Algorithm for Multi-View Data Association

## TL;DR

CLEAR is a novel algorithm that ensures cycle consistency and improves the accuracy and efficiency of multi-view data association in robotics, enabling real-time environment modeling by addressing computational challenges and inconsistency issues.

## Contribution

The paper introduces the CLEAR algorithm, which guarantees cycle consistency and computational efficiency in multi-view data association, outperforming existing methods.

## Key findings

- Demonstrates superior accuracy on synthetic and real datasets.
- Achieves real-time performance in multi-view data association tasks.
- Provides a scalable framework for consistent multi-view alignment.

## Abstract

Many robotics applications require alignment and fusion of observations obtained at multiple views to form a global model of the environment. Multi-way data association methods provide a mechanism to improve alignment accuracy of pairwise associations and ensure their consistency. However, existing methods that solve this computationally challenging problem are often too slow for real-time applications. Furthermore, some of the existing techniques can violate the cycle consistency principle, thus drastically reducing the fusion accuracy. This work presents the CLEAR (Consistent Lifting, Embedding, and Alignment Rectification) algorithm to address these issues. By leveraging insights from the multi-way matching and spectral graph clustering literature, CLEAR provides cycle consistent and accurate solutions in a computationally efficient manner. Numerical experiments on both synthetic and real datasets are carried out to demonstrate the scalability and superior performance of our algorithm in real-world problems. This algorithmic framework can provide significant improvement in the accuracy and efficiency of existing discrete assignment problems, which traditionally use pairwise (but potentially inconsistent) correspondences. An implementation of CLEAR is made publicly available online.