Modest-vocabulary loop-closure detection with incremental bag of tracked words

Abstract A key feature in the context of simultaneous localization and mapping is loop-closure detection, a process determining whether the current robot’s environment perception coincides with previous observation. However, in long-term operations, both computational efficiency and memory requirements involved in an autonomous robot operation in uncontrolled environments, are of particular importance. The majority of approaches scale linearly with the environment’s size in terms of storage and query time. The article at hand presents an efficient appearance-based loop-closure detection pipeline, which encodes the traversed trajectory by a low amount of unique visual words generated on-line through feature tracking. The incrementally constructed visual vocabulary is referred to as the “Bag of Tracked Words.” A nearest-neighbor voting scheme is utilized to query the database and assign probabilistic scores to all visited locations. Exploiting the inherent temporal coherency in the loop-closure task, the produced scores are processed through a Bayesian filter to estimate the belief state about the robot’s location on the map. Also, a geometrical verification step ensures consistency between image matches. Management is also applied to the resulting vocabulary to reduce its growth rate and constraint the system’s computational complexity while improving its voting distinctiveness. The proposed approach’s performance is experimentally evaluated on several publicly available and challenging datasets, including hand-held, car-mounted, aerial, and ground trajectories. Results demonstrate the method’s adaptability, which retains high operational frequency in environments of up to 13 km and high recall rates for perfect precision, outperforming other state-of-the-art techniques. The system’s effectiveness is owed to the reduced vocabulary size, which is at least one order of magnitude smaller than other contemporary approaches. An open research-oriented source code has been made publicly available, which is dubbed as “BoTW-LCD.”