LSH for loop closing detection in underwater visual SLAM

Abstract

Effectiveness in loop closing detection is crucial to increase accuracy in SLAM (Simultaneous Localization and Mapping) for mobile robots. The most representative approaches to visual loop closing detection are based on feature matching or BOW (Bag of Words), being slow and needing a lot of memory resources or a previously defined vocabulary, which complicates and delays the whole process. This paper present a new visual LSH (Locality Sensitive Hashing)-based approach for loop closure detection, where images are hashed to accelerate considerably the whole comparison process. The algorithm is applied in AUV (Autonomous Underwater Vehicles), in several aquatic scenarios, showing promising results and the validity of this proposal to be applied online.