  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.
