LooopDB

In this study, we introduce LoopDB, which is a challenging loop closure dataset comprising of over 1000 images captured across diverse environments, including parks, indoor scenes, parking spaces, as well as centered around individual objects. Each scene is represented by a sequence of five consecutive images. The dataset was collected using a high resolution camera, providing suitable imagery for benchmarking the accuracy of loop closure algorithms, typically used in simultaneous localization and mapping. As ground truth information, we provide computed rotations and translations between eachconsecutive images. Additional to its benchmarking goal, the dataset can be used to train and fine-tune loop closure methodsbased on deep neural networks. LoopDB is publicly available at https://github.com/RovisLab/LoopDB.Index Terms—SLAM benchmarking dataset, Loop closure,Autonomous navigation