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首页> 外文会议>IEEE International Conference on Robotics and Automation >Are We Ready for Service Robots? The OpenLORIS-Scene Datasets for Lifelong SLAM
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Are We Ready for Service Robots? The OpenLORIS-Scene Datasets for Lifelong SLAM

机译：我们准备好服务机器人了吗？终身SLAM的OpenLORIS场景数据集

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Service robots should be able to operate autonomously in dynamic and daily changing environments over an extended period of time. While Simultaneous Localization And Mapping (SLAM) is one of the most fundamental problems for robotic autonomy, most existing SLAM works are evaluated with data sequences that are recorded in a short period of time. In real-world deployment, there can be out-of-sight scene changes caused by both natural factors and human activities. For example, in home scenarios, most objects may be movable, replaceable or deformable, and the visual features of the same place may be significantly different in some successive days. Such out-of-sight dynamics pose great challenges to the robustness of pose estimation, and hence a robot’s long-term deployment and operation. To differentiate the forementioned problem from the conventional works which are usually evaluated in a static setting in a single run, the term lifelong SLAM is used here to address SLAM problems in an ever-changing environment over a long period of time. To accelerate lifelong SLAM research, we release the OpenLORIS-Scene datasets. The data are collected in real-world indoor scenes, for multiple times in each place to include scene changes in real life. We also design benchmarking metrics for lifelong SLAM, with which the robustness and accuracy of pose estimation are evaluated separately. The datasets and benchmark are available online at lifelong-robotic-vision.github.io/dataset/scene.
机译：服务机器人应能够在动态和每日变化的环境中长时间自主运行。虽然同时定位和映射（SLAM）是机器人自治的最基本问题之一，但大多数现有的SLAM作品都是使用短时间记录的数据序列进行评估的。在实际部署中，自然因素和人类活动都可能导致视线外场景变化。例如，在家庭场景中，大多数对象可能是可移动的，可替换的或可变形的，并且同一位置的视觉特征在某些连续的几天中可能会显着不同。这种视线外的动态变化对姿势估计的鲁棒性提出了极大的挑战，因此也给机器人的长期部署和操作带来了挑战。为了将上述问题与通常在单次运行中在静态环境下进行评估的常规工作区分开来，术语“终身SLAM”在此用于解决长期变化的环境中的SLAM问题。为了加速终身SLAM研究，我们发布了OpenLORIS-Scene数据集。数据是在真实的室内场景中收集的，在每个位置多次进行，以包括现实生活中的场景变化。我们还设计了用于终身SLAM的基准度量标准，通过该基准度量标准分别评估了姿态估计的鲁棒性和准确性。数据集和基准可从lifelong-robotic-vision.github.io/dataset/scene在线获得。

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