  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 \textit{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 https://lifelong-robotic-vision.github.io/dataset/scene.
