  This paper proposes a method for tight fusion of visual, depth and inertial
data in order to extend robotic capabilities for navigation in GPS-denied,
poorly illuminated, and texture-less environments. Visual and depth information
are fused at the feature detection and descriptor extraction levels to augment
one sensing modality with the other. These multimodal features are then further
integrated with inertial sensor cues using an extended Kalman filter to
estimate the robot pose, sensor bias terms, and landmark positions
simultaneously as part of the filter state. As demonstrated through a set of
hand-held and Micro Aerial Vehicle experiments, the proposed algorithm is shown
to perform reliably in challenging visually-degraded environments using RGB-D
information from a lightweight and low-cost sensor and data from an IMU.
