  The emergence of data-driven approaches for control and planning in robotics
have highlighted the need for developing experimental robotic platforms for
data collection. However, their implementation is often complex and expensive,
in particular for flying and terrestrial robots where the precise estimation of
the position requires motion capture devices (MoCap) or Lidar. In order to
simplify the use of a robotic platform dedicated to research on a wide range of
indoor and outdoor environments, we present a data validation tool for ego-pose
estimation that does not require any equipment other than the on-board camera.
The method and tool allow a rapid, visual and quantitative evaluation of the
quality of ego-pose sensors and are sensitive to different sources of flaws in
the acquisition chain, ranging from desynchronization of the sensor flows to
misevaluation of the geometric parameters of the robotic platform. Using
computer vision, the information from the sensors is used to calculate the
motion of a semantic scene point through its projection to the 2D image space
of the on-board camera. The deviations of these keypoints from references
created with a semi-automatic tool allow rapid and simple quality assessment of
the data collected on the platform. To demonstrate the performance of our
method, we evaluate it on two challenging standard UAV datasets as well as one
dataset taken from a terrestrial robot.
