| With the popularity of autonomous mobile robots,simultaneous localization and mapping(SLAM)technology in robot localization is becoming more and more important.For visual SLAM algorithms,although most of them have established a good theoretical framework,there are still many challenges in environments such as illumination changes,white walls,and few textures.The problem of map non-reusability caused by illumination change is an inevitable problem for robots in long-term localization.Most of the current localization solutions for illumination changes are combined with deep learning.The purpose of this thesis is to improve the robustness of visual features to illumination changes by optimizing the image transformation model,and to improve the effect of map re-localization in different lighting environments.This thesis proposes an image transformation method based on matching and photometric error(MPT)and integrates it seamlessly into the preprocessing stage of a feature-based visual SLAM framework.Experiments show that the proposed image transformation method has a good effect on improving the matching number of different visual features.In addition,the image transformation module encapsulated in robot operating system(ROS)can be used with multiple visual SLAM systems to improve its re-localization effect in different lighting environments.Faced with the failure of robot tracking and localization in an environment with less texture,this thesis increases the number of tracking features,creates more map data,and optimizes the tracking and localization algorithm by using line features in the SLAM.This thesis proposes a feature extraction and matching algorithm from coarse to fine lines.In the process of tracking and localization,when the matching number of point features is less than the threshold,coarse-grained line feature extraction and matching are performed.In the local mapping process,fine-grained line feature extraction matching is performed after inserting keyframes.The experimental results show that the algorithm increases the number of line features in the map through a certain timeconsuming calculation,which improves the robustness of the tracking and localization process and meets the high real-time requirements of the SLAM system.Finally,a robust visual SLAM system with less texture and illumination changes is designed and implemented based on the algorithm above.On the basis of the open-source visual SLAM framework,the system adds an image transformation module and a map management module,optimizes the localization module,and finally integrates to implement a robust visual SLAM system.The integrated system is tested and evaluated using the campus scene data,which reflects the stability and robustness of the system. |
