Pos2VPR: Fast Position Consistency Validation with Positive Sample Mining for Hierarchical Place Recognition

Visual place recognition (VPR) is a challeng- ing issue for robotics and autonomous systems, focusing on utilizing visual information for robot localization. Currently, hierarchical architecture is being employed by growing works, which embraces RANSAC-based geometric verification for re-ranking. However, RANSAC is time-consuming and only employs geometric information while neglecting other potential information that could be useful for re-ranking. Here we propose a fast position consistency via local patch (PCLP) algorithm to take the position of task-relevant patch-descriptor into account. Without training, it only costs little time but performs better than other re-ranking methods that rely on geometric consistency verification. In this paper, we present a unified place recognition framework that incorporates an aggregation module to extract global features for retrieval and a PCLP validation module to filter local patch for re- ranking. Meanwhile, we propose a RANSAC-based tightly coupled learning (R-TCL) strategy to discover the best positive sample for training robust models. Unlike common sample mining methods, we introduce RANSAC into the sample mining process, achieving trade-off between efficiency and accuracy. Due to improved positive sample mining strategy and novel position validation module, our model is named as Pos2VPR. Remarkably, Pos2VPR outperforms state-of-the-art methods on four major datasets with extremely short running time.