LoopDB：面向大规模同步定位与地图构建的回环检测数据集
LoopDB: A Loop Closure Dataset for Large Scale Simultaneous Localization and Mapping
AI总结：
本文提出面向 SLAM 回环检测的数据集 LoopDB，包含多环境高分辨率连续图像及帧间旋转、平移真值，可用于算法基准测试与深度神经网络训练微调。
摘要：
在本研究中，我们介绍了 LoopDB，这是一个具有挑战性的回环检测数据集，包含在公园、室内场景、停车场以及围绕单个物体等多种环境中采集的 1000 多张图像。每个场景由五张连续图像组成的序列表示。该数据集使用高分辨率相机采集，为评估通常用于同步定位与地图构建的回环检测算法的准确性提供了合适的图像。作为真值信息，我们提供了计算得到的每两张连续图像之间的旋转和平移。除了基准测试目标外，该数据集还可用于训练和微调基于深度神经网络的回环检测方法。LoopDB 公开发布于 https://github.com/RovisLab/LoopDB。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
