A Reliable Linearly Separable Optimization Method for SLAM Estimation based on Stereo Features

Abstract

SLAM (Simultaneous Localization and Mapping) based on graphical representation is used to solve optimization non-linear problems. The solutions of graph-based SLAM have n noisy measurements and re-projection errors lead to uncertainty in robot pose and landmark position. The proposed system providesa novel two-stage architecture for visual SLAM that was designed and implemented. The new two-stage architecture resulted in an exactly linear graph optimization, by using a linear optimization method for graph optimization, so the problem now becomes linear. We take benefit of all camera measurements and needn’t ignore any frames. We enhance accuracy of large scale VSLM system, minimize processing time and decrease computational cost. We show that our system can be used with a higher accuracy and processing time than well-known systems based on bundle adjustment.