构建1000+张图像的闭环检测数据集，用于高精度定位与建图算法评测。
LoopDB: A Loop Closure Dataset for Large Scale Simultaneous Localization and Mapping
- 采集超过1000张多场景图像，每场景5张连续帧
- 提供连续图像间的精确旋转与平移真值
- 支持深度学习模型训练与闭环检测算法测试
本文提出LoopDB，一个包含1000多张图像的大规模闭环检测数据集，覆盖公园、室内、停车场及单个物体周围等多种环境。每个场景由连续五张图像构成，采用高分辨率相机采集，适用于评估同时定位与地图构建（SLAM）中闭环检测算法的准确性。数据集提供连续图像间的精确旋转与平移真值，可用于算法基准测试，也可用于基于深度神经网络的闭环检测方法训练与微调。该数据集已公开于https://github.com/RovisLab/LoopDB。
原文摘要 · Abstract (English)
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.
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