Aiming at the problem that mobile robots are prone to localization errors during global relocation

this paper proposes a particle filter relocation method based on the constructed semantic map and loading semantic likelihood estimation to solve the problem. Using the lidar to establish the environmental grid map

meanwhile combing with the three-dimensional depth camera’s object recognition and positioning information

the environmental semantic map is obtained by giving semantic information. During the relocation process

the particle filter method is used to simultaneously match the grid map structure and the semantic information of the environment to calculate the actual position of the robot on the map

and accurately to realize the position relocation. Experiments results show that this method can overcome the shortcomings of the original particle filtering method that only uses environmental structure information for matching

also solve the problem of robot global relocation error-prone and enhance the robustness of relocation

and enhances the convergence speed of relocation.