Monocular Parallel Tracking and Mapping with Odometry Fusion for MAV Navigation in Feature-lacking Environments

Despite recent progress, autonomous navigation on Micro Aerial Vehicles with a single frontal camera is still a challenging problem, especially in feature-lacking environments. 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 quadrotor flying in a typical feature-lacking indoor environment.