###### Abstract  
In order to solve the path planning problem of an intelligent vehicle in an unknown environment, this paper proposes a map construction and path planning method for mobile robots based on multi-sensor information fusion. Firstly, the extended Kalman filter (EKF) is used to fuse the ambient information of LiDAR and a depth camera. The pose and acceleration information of the robot is obtained through the pose sensor. The SLAM algorithm based on a fusion of LiDAR, a depth camera, and the inertial measurement unit was built. Secondly, the improved ant colony algorithm was used to carry out global path planning. Meanwhile, the dynamic window method was used to realize local planning and local obstacle avoidance. Finally, experiments were carried out on a robot platform to verify the reliability of the proposed method. The experiment results showed that the map constructed by multi-sensor information fusion was closer to the real environment, and the accuracy and robustness of SLAM were effectively improved. The turning angle of the path was smoothed using the improved ant colony algorithm, and the real-time obstacle avoidance was able to be realized using the dynamic window method. The efficiency of path planning was improved, and the automatic feedback control of the intelligent vehicle was able to be realized. map construction; data fusion; ant colony algorithm; path planning; dynamic window +
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[PERSON] et al., proposed a binocular visual-inertial odometry method based on a semi-direct method. This method combines the inertial navigation data and the visual information of the binocular camera to construct a high-precision map, which reduces the calculation of measurement values and improves the work efficiency of the system [5]. [PERSON] et al., used a graph-based optimization method to fuse LiDAR and IMU data. This method improves the accuracy of map construction and location [6]. [PERSON] et al., adopted camera information and used inter-frame matching to constrain the data association between point clouds and improved the motion trajectory estimation method by iterating the closest points. However, the accuracy of constructing maps was poor [7]. [PERSON] et al., installed the 2D LiDAR vertically on the robot and matched it with a depth camera for data collection to construct a high-precision map, but the positioning accuracy of this method is poor [8]. Multi-sensor fusion can provide better data support for mobile robot positioning mapping and make up for the defects of single-sensor detection. Path planning is based on multi-source perception information. The feasible path to the destination is searched for with a certain update frequency, and an optimal target path is iterated from the initial point to the destination on the premise of ensuring safety. Among the path planning methods, the two-dimensional geometric search method is the most mature path planning method. In addition, there are some widely used algorithms, such as Dijkstra [9], A* [10], and Basic Theta* [11]. [PERSON] et al. proposed a large-scale adaptive ocean V-ACO path planner to improve the heuristic function of the ant colony algorithm to find the collision-free optimal trajectory. This method can find the optimal path with more sampling values [12]. [PERSON] et al., proposed the dynamic obstacle dynamic window algorithm and the dynamic obstacle tree dynamic window algorithm to improve the safety of avoidance operations using two restricted occupancy grids [13]. [PERSON] et al. proposed a hybrid path planning algorithm based on the A* algorithm and the dynamic window method. This method solves the problem of path planning in a complex multi-objective environment and improves the efficiency of autonomous navigation [14]. [PERSON] et al. proposed a hybrid path planning algorithm combining the A* algorithm and the dynamic window method. The resulting path is smoother, has global optimality, and improves dynamic obstacle avoidance and autonomous navigation capabilities [15].