Beyond SIFT using binary features in Loop Closure Detection

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) [1] 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.

Authors

• Lei Han
• Guyue Zhou
• Lan Xu 0003
• Lu Fang 0001

Keywords

• Feature extraction
• Liquid crystal displays
• Visualization
• Hamming distance
• Simultaneous localization and mapping
• Complexity theory
• Cameras
• Loop Closure
• Loop Closure Detection
• Similarity Measure
• Nearest Neighbor Search
• Burstiness
• Bayesian Inference
• Image Features
• Local Features
• Image Size
• Descriptive Characteristics
• Probability Of Events
• Hash Function
• Feature Pairs
• Global Signature
• High Similarity Score
• Visual Simultaneous Localization And Mapping
• Locality Sensitive Hashing
• Indoor Navigation