PIRF-Nav 2.0: Fast and online incremental appearance-based loop-closure detection in an indoor environment

Abstract

This paper presents a fast and online incremental solution for an appearance-based loop-closure detection problem in a dynamic indoor environment. Closing the loop in a dynamic environment has been an important topic in robotics for decades. Recently, PIRF-Nav has been reported as being successful in achieving high recall rate at precision 1. However, PIRF-Nav has three main disadvantages: (i) the computational expense of PIRF-Nav is beyond real-time, (ii) it utilizes a large amount of memory in the redundant process of keeping signatures of places, and (iii) it is ill-suited to an indoor environment. These factors hinder the use of PIRF-Nav in a general environment for long-term, high-speed mobile robotic applications. Therefore, this paper proposes two techniques: (i) new modified PIRF extraction that makes the system more suitable for an indoor environment and (ii) new dictionary management that can eliminate redundant searching and conserve memory consumption. The results show that our proposed method can complete tasks up to 12 times faster than PIRF-Nav with only a slight percentage decline in recall. In addition, we collected additional data from a university canteen crowded during lunch time. Even in this crowded indoor environment, our proposed method has better real-time processing performance compared with other methods.