Beyond SIFT using Binary features for Loop Closure Detection

TL;DR

This paper introduces a binary feature-based loop closure detection method that outperforms SIFT-based approaches in accuracy while maintaining real-time performance, by improving texture handling and analyzing hashing parameters.

Contribution

The paper presents a novel binary feature-based LCD system with burstiness handling and a theoretical analysis of Multi-Index Hashing for enhanced accuracy and parameter optimization.

Findings

Achieved higher precision-recall than SIFT-based methods.

Operates at 30Hz on large datasets.

Provides theoretical insights for hashing parameter selection.

Abstract

In this paper a binary feature based Loop Closure Detection (LCD) method is proposed, which for the first time achieves higher precision-recall (PR) performance compared with state-of-the-art SIFT feature based approaches. The proposed system originates from our previous work Multi-Index hashing for Loop closure Detection (MILD), which employs Multi-Index Hashing (MIH)~\cite{greene1994multi} for Approximate Nearest Neighbor (ANN) search of binary features. As the accuracy of MILD is limited by repeating textures and inaccurate image similarity measurement, burstiness handling is introduced to solve this problem and achieves considerable accuracy improvement. Additionally, a comprehensive theoretical analysis on MIH used in MILD is conducted to further explore the potentials of hashing methods for ANN search of binary features from probabilistic perspective. This analysis provides more freedom on best parameter choosing in MIH for different application scenarios. Experiments on popular public datasets show that the proposed approach achieved the highest accuracy compared with state-of-the-art while running at 30Hz for databases containing thousands of images.