论文标题
用立体声V-SLAM：数据集，指标和基线来检测点云更改检测
Point Cloud Change Detection With Stereo V-SLAM:Dataset, Metrics and Baseline
论文作者
论文摘要
本地化和导航是基本的机器人任务，需要精确，最新的地图完成这些任务，众包数据可检测地图更改，提出了吸引人的解决方案。收集和处理众包数据需要低成本的传感器和算法，但是现有的方法依赖于昂贵的传感器或计算昂贵的算法。此外，没有现有数据集来评估点云更改检测。因此，本文提出了一个使用低成本传感器（例如立体声摄像机和IMU）来检测点云图中的变化的新型框架。此外，我们创建一个数据集和相应的指标，借助高保真模拟器虚幻引擎4。实验表明，基于视觉的框架可以有效地检测数据集中的更改。
Localization and navigation are basic robotic tasks requiring an accurate and up-to-date map to finish these tasks, with crowdsourced data to detect map changes posing an appealing solution. Collecting and processing crowdsourced data requires low-cost sensors and algorithms, but existing methods rely on expensive sensors or computationally expensive algorithms. Additionally, there is no existing dataset to evaluate point cloud change detection. Thus, this paper proposes a novel framework using low-cost sensors like stereo cameras and IMU to detect changes in a point cloud map. Moreover, we create a dataset and the corresponding metrics to evaluate point cloud change detection with the help of the high-fidelity simulator Unreal Engine 4. Experiments show that our visualbased framework can effectively detect the changes in our dataset.
