MILD: Multi-Index hashing for Loop closure Detection
This work addresses a bottleneck in visual SLAM and robot relocalization for robotics applications, offering an incremental improvement over existing binary feature methods.
The paper tackles the low recall problem in loop closure detection for visual SLAM and robot relocalization by proposing MILD, a real-time approach using multi-index hashing for direct feature matching, achieving high recall without extra computational complexity and demonstrating superiority in efficiency and accuracy over state-of-the-art methods.
Loop Closure Detection (LCD) has been proved to be extremely useful in global consistent visual Simultaneously Localization and Mapping (SLAM) and appearance-based robot relocalization. Methods exploiting binary features in bag of words representation have recently gained a lot of popularity for their efficiency, but suffer from low recall due to the inherent drawback that high dimensional binary feature descriptors lack well-defined centroids. In this paper, we propose a realtime LCD approach called MILD (Multi-Index Hashing for Loop closure Detection), in which image similarity is measured by feature matching directly to achieve high recall without introducing extra computational complexity with the aid of Multi-Index Hashing (MIH). A theoretical analysis of the approximate image similarity measurement using MIH is presented, which reveals the trade-off between efficiency and accuracy from a probabilistic perspective. Extensive comparisons with state-of-the-art LCD methods demonstrate the superiority of MILD in both efficiency and accuracy.