(Georgia Institute of Technology, 2013-11)
Ta, Duy-Nguyen; Ok, Kyel; Dellaert, Frank
Despite recent progress, autonomous navigation
on Micro Aerial Vehicles with a single frontal camera is still
a challenging problem, especially in feature-lacking environ-
ments. On a mobile robot with a frontal camera, monoSLAM
can fail when there are not enough visual features in the scene,
or when the robot, with rotationally dominant motions, yaws
away from a known map toward unknown regions. To overcome
such limitations and increase responsiveness, we present a
novel parallel tracking and mapping framework that is suitable
for robot navigation by fusing visual data with odometry
measurements in a principled manner. Our framework can
cope with a lack of visual features in the scene, and maintain
robustness during pure camera rotations. We demonstrate our
results on a dataset captured from the frontal camera of a quad-
rotor flying in a typical feature-lacking indoor environment.