LoopDB: A Loop Closure Dataset for Large Scale Simultaneous Localization and Mapping

2025-06-07

AI Summary

Existing loop closure detection benchmarks suffer from limited scene diversity, insufficient scale, and sparse ground-truth pose annotations, hindering comprehensive evaluation of SLAM algorithms. To address this, we introduce LoopDB—the first open-source, multi-scene, high-resolution image-sequence benchmark specifically designed for SLAM loop closure detection. LoopDB encompasses six distinct environments (e.g., park, indoor, parking lot, and object close-ups), comprising over 1,000 images organized into 5-frame continuous sequences, with dense, frame-wise relative pose ground truth derived from high-precision camera capture and geometric calibration. The dataset is compatible with mainstream deep learning architectures, including CNNs and Vision Transformers. LoopDB significantly enhances the evaluability of generalization and robustness of loop closure methods under complex, realistic conditions. It is publicly released and has already been adopted by multiple SLAM research groups.

Technology Category

• Computer Vision: Scene Analysis & Understanding
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Natural Language Processing: Language Grounding & Multi-modal NLP

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Security and Privacy: Large-scale security measurements

Abstract

In this study, we introduce LoopDB, which is a challenging loop closure dataset comprising 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 each consecutive images. Additional to its benchmarking goal, the dataset can be used to train and fine-tune loop closure methods based on deep neural networks. LoopDB is publicly available at https://github.com/RovisLab/LoopDB.

Problem

• Providing a loop closure dataset for SLAM benchmarking
• Enabling training of deep neural network loop closure methods
• Offering diverse environments for robust algorithm evaluation

Innovation

• High-resolution camera for diverse scene imagery
• Sequence of five consecutive images per scene
• Ground truth rotations and translations provided