Image based optimisation without global consistency for constant time monocular visual SLAM

This paper presents a monocular visual SLAM system that does not require a globally consistent 3D model. Instead of generating a globally consistent 3D model and localising the camera from the 3D model, the system merely optimises relative pose parameters for pairs of keyframes that overlap on the scene, providing accurate local information at the expense of global consistency. During run-time, the camera is localised using only 2D measurements from nearby keyframes instead of using correspondences between 2D measurements and 3D features of a 3D model. Extensive experiments using both synthetic and real data sets were performed to evaluate the system's performance. Results show that our system is accurate and runs in real time at an average of 25 frames per second on a standard computer. Finally, we also show how useful applications can be easily developed on top of a framework without global consistency.