Online learning of binary feature indexing for real-time SLAM relocalization

Abstract

In this paper, we propose an indexing method for approximate nearest neighbor search of binary features. Being different from the popular Locality Sensitive Hashing (LSH), the proposed method construct the hash keys by an online learning process instead of pure randomness. In the learning process, the hash keys are constructed with the aim of obtaining uniform hash buckets and high collision rates, which makes the method more efficient on approximate nearest neighbor search than LSH. By distributing the online learning into the simultaneous localization and mapping (SLAM) process, we successfully apply the method to SLAM relocalization. Experiments show that camera poses can be successfully recovered in real time even there are tens of thousands of landmarks in the map.