Environment maps are essential for robots and intelligent gadgets to autonomously carry out tasks. Traditional maps built by visual sensors include metric ones and topological ones. These maps are navigation-oriented and not adequate for service robots or intelligent gadgets to interact with or serve human users who normally rely on conceptual knowledge or semantic contents of the environment. Therefore, semantic maps become necessary for building an effective human-robot interface. Although researchers from both robotics and computer vision domains have designed some promising systems, mapping with high accuracy and how to use semantic information for localization remain challenging.
This thesis describes several novel methodologies to address these problems. RGB-D visual data is used as system input. Deep learning techniques and SLAM algorithms are combined in order to achieve better system performance. Firstly, a traditional feature based semantic mapping approach is presented. A novel matching error rejection algorithm is proposed to increase both loop closure detection and semantic information extraction accuracy. Evaluational experiments on public benchmark dataset are carried out to analyze the system performance. Secondly, a visual odometry system based on a Recurrent Convolutional Neural Network is presented for more accurate and robust camera motion estimation. The proposed network deploys an unsupervised end-to-end framework. The output transformation matrices are on an absolute scale, i.e. true scale in the real world. No data labeling or matrix post-processing tasks are required. Experiments show the proposed system outperforms other state-of-the-art VO systems. Finally, a novel topological localization approach based on the pre-built semantic maps is presented. Two streams of Convolutional Neural Networks are applied to infer locations. The additional semantic information in the maps is inversely used to further verify localization results. Experiments show the system is robust to viewpoint, lighting condition and object changes
