This paper introduces GUMS, a complete projection model for omnidirectional stereo vision systems. GUMS is based on the existing generalized unified model (GUM), which we extend in order to satisfy a tight relationship among a pair of omnidirectional views for fixed baseline sensors. We exemplify the proposed model's calibration via a single-camera coaxial omnistereo system in a joint bundle-adjusted fashion. We compare our coupled method against the naive approach where the calibration of intrinsic parameters is first performed individually for each omnidirectional view using existing monocular implementations, to then solve for the extrinsic parameters as an additional step that has no effect on the intrinsic model solutions initially computed. We validate GUMS and its calibration effectiveness using both real and synthetic systems against ground-truth data. Our calibration method proves successful for correcting the unavoidable misalignment present in vertically-configured catadioptric rigs. We also generate 3D point clouds employing the calibrated GUMS systems in order to demonstrate the qualitative outcome of our contribution.

In this paper we present a navigation system for Micro Aerial Vehicles (MAV) based on information provided by a visual odometry algorithm processing data from an RGB-D camera. The visual odometry algorithm uses an uncertainty analysis of the depth information to align newly observed features against a global sparse model of previously detected 3D features. The visual odometry provides updates at roughly 30 Hz that is fused at 1 KHz with the inertial sensor data through a Kalman Filter. The high-rate pose estimation is used as feedback for the controller, enabling autonomous flight. We developed a 4DOF path planner and implemented a real-time 3D SLAM where all the system runs on-board. The experimental results and live video demonstrates the autonomous flight and 3D SLAM capabilities of the quadrotor with our system.

An RGB-D camera is a sensor which outputs color and depth and information about the scene it observes. In this paper, we present a real-time visual odometry and mapping system for RGB-D cameras. The system runs at frequencies of 30Hz and higher in a single thread on a desktop CPU with no GPU acceleration required. We recover the unconstrained 6-DoF trajectory of a moving camera by aligning sparse features observed in the current RGB-D image against a model of previous features. The model is persistent and dynamically updated from new observations using a Kalman Filter. We formulate a novel uncertainty measure for sparse RGD-B features based on a Gaussian mixture model for the filtering stage. Our registration algorithm is capable of closing small-scale loops in indoor environments online without any additional SLAM back-end techniques.