Dense 3D Semantic SLAM of traffic environment based on stereo vision

To solve the intelligent vehicles' problems of 'where am I?' and 'what is around me?', a dense 3D sematic Simultaneous Localization and Mapping (SLAM) system is proposed to evaluate the pose of the intelligent vehicles and build the dense 3D semantic map. We address these challenges by combining a state of art Stereo-ORB-SLAM system and Convolutional Neural Networks. Firstly, we build a dense 3D point cloud map by using a four thread Stereo-ORB-SLAM system. Subsequently, a fully convolutional neural network architecture which uses RGB-D image as input is used to obtain pixel-wise segmentation. Finally, we fuse the geometric information and semantic information to get the semantic map. We test our method on the KITTI dataset and our dataset made with the Fpgalena stereo camera. Results indicate the system was effective in the real-time building of a semantic map, the speed of the entire system is about 10Hz, and the loop closing function can eliminate most of the drifting errors.