Probabilistic Appearance-Based Place Recognition Through Bag of Tracked Words

摘要

A key feature in robotics applications is to recognize whether the current environment observation corresponds to a previously visited location. Should the place be recognized by the robot, a Loop Closure Detection (LCD) has occurred. The letter in hand deploys a novel low complexity LCD method based on the representation of the route by unique visual features (VFs). Each of these VFs, referred to as “Tracked Word” (TW), is generated on-line through a tracking technique coupled with a guided feature-detection mechanism and belongs to a group of successive images. During the robot's navigation, new TWs are added to the database forming a bag of tracked words. When querying the database seeking for loop closures, the new local-feature descriptors are associated with the nearest neighboring TWs in the map casting votes to the corresponding instances. The system relies on a probabilistic method to select the most suitable loop closing pair, based on the number of votes each location polls. The proposed system depends solely on the appearance information of the scenes on the trajectory, without requiring any pre-training phase. The evaluation of the method is administered via a variety of tests with several community datasets, thus proving its capability of achieving high recall rates for perfect precision.