Abstract: Visual-based localization and mapping is the key technology for autonomous robots.Visual localization and mapping in agricultural environments faces more challenges,including few distinguishable landmarks for tracking,large-scale scene,unstable movements.To address these problems,a stereo visual localization and mapping method is proposed.Static stereo matching points are used to increase the number andcoverage of map points,which improve the accuracy of depth calculation.A point selection method is proposed to further improve the accuracy and efficiency by sampling the dense map points and removing outliers.Then scale estimation is proposed to reduce the scale error of localization and mapping in large scale agricultural scenes.Keyframe criteria is adapted to avoid the impact of large far-away objects that could cause abnormal keyframe distribution.Finally,a new motion assumption is proposed to recover the system from failure tracking,which improves the system's robustness at the case of unstable movements.Experimental results show that the proposed method achieves better performance than other state-of-the-art vi-sual localization and mapping systems.By addressing the challenges individually,the proposed visual localization and mapping system is more accurate and robust in agricultural environments.

Key words: Agricultural environments, Visual localization and mapping, Direct method, Stereo vision, Scale estimation