An SfM Algorithm With Good Convergence That Addresses Outliers for Realizing Mono-SLAM

Monocular simultaneous localization and mapping (mono-SLAM) is a key component of autonomous robot visual navigation. Recently, the structure from motion (SfM) approach has become an attractive means of implementing mono-SLAM because of its high accuracy; although, in this application, the SfM approach must be operable in real time and robust to outliers. However, because of strong nonlinearity, conventional SfM methods, such as bundle adjustment, must consider multiple initial values to obtain the globally optimal result, which is time consuming. In this paper, a novel iterative SfM algorithm based on the object-space objective function is proposed. To improve the method's robustness to outliers and to incorporate the information obtained from other types of sensors, an approach to closely integrate the proposed SfM algorithm with extended Kalman filter-based SLAM is proposed. Experimental results using both synthesized and real data are consistent with our theory, and verify that the main advantage of the proposed SfM algorithm is its good convergence. The algorithm is therefore particularly appropriate for realizing mono-SLAM, because when rotations can be obtained approximately using gyroscope information, the algorithm is globally convergent from any initial value.