In recent years, the work on simultaneous localization and mapping has matured significantly. Robust techniques have been developed to explore and map a static environment in real-time. However, the problem of localizing and mapping a dynamic environment is still to be solved. The dynamic part of the environment not only makes the localization difficult but it introduces a diverse set of challenges to the existing problems such as detecting, tracking and segmenting the moving objects, and 3D reconstruction of the moving objects and/or static environment.
This thesis focuses on studying the problem of simultaneously localizing and mapping an actively dynamic environment. A comprehensive review and analysis of the state-of-the-art methods are provided for both static and dynamic cases. A stereo camera is used to explore the dynamic environment and obtain semi-dense point clouds for the image sequence. The proposed approach is a variant of the standard ICP where the outliers of the registration process are not discarded. All 3D points are assigned a confidence measure based on their association in their respective neighborhood. The confidence measure decides if a 3D point is classified static or dynamic in the global map. Hence, the approach does not require any prior information about the environment or the moving objects. In the latter part of this study, the moving objects are segmented in 3D space and 2D images for any potential future analysis. The framework is tested with highly dynamic scenes from both indoor and outdoor environments. The results demonstrate the effectiveness of the proposed approach
