Hybrid Semi-Dense 3D Semantic-Topological Mapping From Stereo Visual-Inertial Odometry SLAM With Loop Closure Detection

Intelligent mobile vehicles asks for the ability to learn to comprehend immediate surroundings similar to human cognition and autonomously navigate in an unknown scene. Although simultaneous localization and mapping (SLAM) can construct a geometrically surrounding map, the map contains little semantic information. This paper presents a novel stereo visual-inertial system with loop closure detection based on a semantic-topological map framework. In our system, a hybrid 3D point cloud semantic-topological mapping framework is used to realize autonomous navigation simply by providing a map for path planning and meanwhile for storing semantic information under a dynamic environment. A stereo visual-inertial system can obtain more accurate visual-inertial odometry than a monocular system, and a visual-inertial system is capable of real-time map correction when the localization module detects a loop closure. This paper adopts state-of-the-art Mask R-CNN components to obtain 2D semantic information and project it to a semi-dense 3D semantic-topological map built by stereo visual-inertial SLAM. We perform experimental evaluation of our proposed system on the EuRoC MAV datasets and we also compare it with other competitive methods. Our results demonstrate that the proposed method is more effective than the other competitive methods.