The gold standard method for tridimensional reconstruction and camera localization from a set of images is well known to be Bundle Adjustment (BA). Although BA was regarded for years as a costly method restricted to the offline domain, several real time algorithms based on BA flourished in the last decade. However those algorithms were limited to perform SLAM in small scenes or only Visual Odometry. In this work we built on excellent algorithms of the last years to design from scratch a Monocular SLAM system that operates in real time, in small and large, indoor and outdoor environments, with the capability of wide baseline loop closing and relocalization, and including full automatic initialization. Our survival of the fittest approach to select the points and keyframes of the reconstruction generates a compact and trackable map that only grows if the scene content changes, enhancing lifelong operation. We present an exhaustive evaluation in 27 sequences from the most popular datasets achieving unprecedented performance with a typical localization accuracy from 0.2% to 1% of the trajectory dimension in scenes from a desk to several city blocks. We make public a ROS implementation.
Mur-Artal et al. REF proposed a feature-based monocular ORB-SLAM system, which selected the points and keyframes for reconstruction and obtained an excellent performance.
ORB-SLAM: a Versatile and Accurate Monocular SLAM System
IEEE Transactions on Robotics
IEEE Transactions on Robotics
Computer Science Mathematics