This work is the state-of-the-art of monocular SLAM.
Abstract
We present a novel and general optimisation framework for visual SLAM, which scales for both local, highly accurate reconstruction and large-scale motion with long loop closures. We take a two-level approach that combines accurate pose-point constraints in the primary region of interest with a stabilising periphery of pose-pose soft constraints. Our algorithm automatically builds a suitable connected graph of keyposes and constraints, dynamically selects inner and outer window membership and optimises both simultaneously. We demonstrate in extensive simulation experiments that our method approaches the accuracy of offline bundle adjustment while maintaining constant-time operation, even in the hard case of very loopy monocular camera motion. Furthermore, we present a set of real experiments for various types of visual sensor and motion, including large scale SLAM with both monocular and stereo cameras, loopy local browsing with either monocular or RGB-D cameras, and dense RGB-D object model building.
Reference
Raúl Mur-Artal, J. M. M. Montiel and Juan D. Tardós."ORB-SLAM: A Versatile and Accurate Monocular SLAM System." IEEE Transactions on Robotics, vol. 31, no. 5, pp. 1147-1163, October 2015.(TOR 2015).
Departamento de Informática e Ingeniería de Sistemas,
Escuela de Ingeniería y Arquitectura, Universidad de Zaragoza
2016-03-29