Aiming to address the problems of insufficient robustness and high computational cost of the Cartographer algorithm during indoor relocation, a relocation method was proposed that enhanced Cartographer by incorporating semantic information, thereby improving robot relocation performance in complex indoor environments. Semantic object information from the robot’s surroundings was extracted by using an RGB-D camera and deep learning techniques, and was subsequently mapped into a structured point cloud. The extracted semantic point cloud was then fused with the grid map constructed by the Cartographer algorithm through projection transformation to generate a complete and informative 2D semantic grid map. A semantic object relationship linked list was also built based on the extracted semantic information to represent inter-object spatial context. During the relocation process, the semantic information provided by the 2D semantic grid map was used to offer a prior pose estimation for the robot, narrowing the search space of the Cartographer algorithm, reducing the number of iterations, and enabling rapid and efficient relocation. Experiments conducted in real indoor scenarios validated the effectiveness of the proposed method. The results showed that, in similar environments, the proposed method improved real-time performance by 49.78% and 78.27% compared with that of the original Cartographer and AMCL algorithms, respectively. In degraded scenarios, improvements reached 76.18% and 83.96%, respectively. Moreover, the relocation success rate was increased by over 75% on average. In addition, the constructed 2D semantic grid map supported semantic navigation and planning, demonstrating promising potential for application in service robots and related fields.