Simultaneous Localisation and Mapping (SLAM) provide a novel solution for the robots to localise and navigate an unknown environment. Initial SLAM research focused mainly on the indoor environment, assuming the background to be primarily static. In contrast, the real world has dynamic interactions that restrict the implementation of SLAM to limited scenarios. This brings a higher requirement to deal with the moving objects in dynamic environments for robust SLAM performance.

Semantic understanding of the environment helps in filtering out the influence of dynamic objects in the vicinity. An instance segmentation based on two-stage neural architecture is used for this purpose, which is hard to operate in real-time navigation. In this project, the benefits of single stage neural architecture are studied in terms of speed and accuracy for improving the efficiency of dynamic features removal in the application of SLAM.

Although Instance segmentation architecture helps to identify the potentially dynamic object by learning from the dataset, it cannot differentiate moving objects from non-moving objects in the dynamic class. Hence, all the features corresponding to the predicted dynamic class are removed even when the objects remain stationary, affecting the quality of SLAM performance. A two-stream encoder-decoder architecture is developed to segment the moving masks using RGB and optical flow input, improving feature tracking without affecting robustness. The feasibility of encoding dynamic information to enhance quality semantic mapping is also studied.