Analyzing different approaches to Visual SLAM in dynamic environments : A comparative study with focus on strengths and weaknesses

Abstract: Simultaneous Localization and Mapping (SLAM) is the crucial ability for many autonomous systems to operate in unknown environments. In recent years SLAM development has focused on achieving robustness regarding the challenges the field still faces e.g. dynamic environments. During this thesis work different existing approaches to tackle dynamics with Visual SLAM systems were analyzed by surveying the recent literature within the field. The goal was to define the advantages and drawbacks of the approaches to provide further insight into the field of dynamic SLAM. Furthermore, two methods of different approaches were chosen for experiments and their implementation was documented. Key conclusions from the literature survey and experiments are the following. The exclusion of dynamic objects with regard to camera pose estimation presents promising results. Tracking of dynamic objects provides valuable information when combining SLAM with other tasks e.g. path planning. Moreover, dynamic reconstruction with SLAM offers better scene understanding and analysis of objects’ behavior within an environment. Many solutions rely on pre-processing and heavy hardware requirements due to the nature of the object detection methods. Methods of motion confirmation of objects lack consideration of camera movement, resulting in static objects being excluded from feature extraction. Considerations for future work within the field include accounting for camera movement for motion confirmation and producing available benchmarks that offer evaluation of the SLAM result as well as the dynamic object detection i.e. ground truth for both camera and objects within the scene.