Visual Odometry and 3D Mapping in Indoor Environments

This paper presents a robust state estimation and 3D environment modeling approach that enables Micro Aerial Vehicle (MAV) operating in challenging GPS-denied indoor environments. A fast, accurate and robust approach to visual odometry is developed based on Microsoft Kinect. Discriminative features are extracted from RGB images and matched across consecutive frames. A robust least-square estimator is applied to get relative motion estimation. All computation is performed in real-time, which provides high frequency of 6 degree-of-freedom state estimation. A detailed 3D map of an indoor environment is also constructed.