The ability of intelligent unmanned platforms to achieve autonomous navigation and positioning in a large-scale environment has become increasingly demanding, in which LIDAR-based Simultaneous Localization and Mapping (SLAM) is the mainstream of research schemes. However, the LIDAR-based SLAM system will degenerate and affect the localization and mapping effects in extreme environments with high dynamics or sparse features. In recent years, a large number of LIDAR-based multi-sensor fusion SLAM works have emerged in order to obtain a more stable and robust system. In this work, the development process of LIDAR-based multi-sensor fusion SLAM and the latest research work are highlighted. After summarizing the basic idea of SLAM and the necessity of multi-sensor fusion, this paper introduces the basic principles and recent work of multi-sensor fusion in detail from four aspects based on the types of fused sensors and data coupling methods. Meanwhile, we review some SLAM datasets and compare the performance of five open-source algorithms using the UrbanNav dataset. Finally, the development trend and popular research directions of SLAM based on 3D LIDAR multi-sensor fusion are discussed and summarized.

In order to avoid the problem of inaccurate positioning of single indoor sensor, an adaptive federated Kalman filter (AFKF) algorithm is proposed in this paper. According to the error covariance matrix, residual, trace and other information of each subfilter, the sharing factors of information fusion and distribution in federated Kalman filter are adaptively adjusted. And the fault detector is added to detect extreme abnormal condition. The simulation was performed and the experiment was carried out in indoor environment. The results show that the AFKF is not affected by abnormal signals of the subfilter, and the accuracy compared with other AFKF algorithms is improved by 10.76% and 10.84% in ${X}$ and ${Y}$ directions. And even if the subfilter fails the proposed AFKF can also work normally. All of these indicates the feasibility and effectiveness of the proposed AFKF.

With the rapid development of mobile robots, environmental perception based on a single sensor can hardly meet the task requirements of the robots for object detection and path planning in complex scenarios. In this article, an object detection fusion algorithm based on both the information of the LiDAR point cloud and the camera image is proposed. First, YOLOv4 is used to detect the objects in the image. Then, the point cloud is projected into the image, and the target point cloud is filtered out according to the range of the 2-D detection frame. The target point cloud is used to perform density clustering and generate the output of a bounding box with semantic labels. Meanwhile, the original point cloud data are processed by an improved four-neighborhood clustering algorithm based on the Euclidean distance and angle threshold to generate the output of another bounding box without semantic labels. Finally, the clusters obtained by different methods are fused and judged to produce the output of the final object detection results. The test using the KITTI dataset shows that the accuracy of the improved four-neighbor clustering algorithm is increased to 0.835. The final semantic segmentation results have an average positioning error of 0.033 and 0.073 m in the $x$ - and $y$ -directions. The average angular error of the vehicle direction is 0.90°. Compared with the other two types of point cloud segmentation networks, our approach has the highest accuracy and sufficient real-time performance, which can reach 9.96 Hz in the experiment.

Two-dimensional (2D) simultaneous localization and mapping (SLAM) is a key technology for intelligent indoor robots. By using a map generated via SLAM, the robot can navigate and perform specific tasks. This paper reviews the progress of 2D Lidar SLAM algorithms based on four principles: filter-based SLAM, matching-based SLAM, graph optimization-based SLAM, and deep learning-based SLAM, highlighting their advantages, disadvantages, and applicability. Additionally, two key research topics in 2D Lidar SLAM are presented: solutions for dynamic objects during mapping and the fusion of 2D Lidar and vision data. Finally, the development trends of 2D SLAM are discussed.

The positioning accuracy has a significant impact on the application of the robotic arm. In this study, a novel method based on improved manta ray foraging optimization algorithm is presented for kinematic parameters calibration, which can reduce the absolute positioning error of the manipulator. The error model of the robot is established according to the Denavit-Hartenberg (D-H) method. The redundancy of the model is analyzed, and the redundant parameters are eliminated. The kinematic parameter identification of the robot is transformed into a nonlinear optimization problem. The original manta ray foraging optimization algorithm is improved by using the natural selection strategy and adaptive parameter control strategy to improve searching performance and convergence speed. The calibration experiment of the self-designed 6-DOF robot arm is carried out. A measurement method for measuring the position of the robot end using a 3D scanner is introduced. The improved manta ray foraging optimization algorithm (IMRFO) is used to identify the kinematic parameter errors of the robot. The experimental results show that by using the improved manta ray foraging optimization algorithm to calibrate the parameter error, the position error of the robot end is reduced from 10.2357 to 0.5483mm, or by 94.6%. The results show that the IMRFO algorithm proposed in this paper has high identification accuracy and fast convergence speed, which can effectively identify the kinematic parameter error of the robot.

In this paper, model predictive control (MPC) based on an adaptive neural-fuzzy inference system (ANFIS) is proposed to realize control of an omni-directional service robot in path tracking. The weight of the cost function in a traditional MPC needs to be manually adjusted, and it is difficult to adjust to a satisfactory value. In order to improve the performance and the control accuracy of MPC, a fuzzy system trained by ANFIS is used to adaptively adjust the weight of MPC’s cost function to reduce the error in the process of path tracking. The different simulation experiments are conducted to verify the performance of the proposed algorithm. The experimental results show that the distance error of MPC based on ANFIS is reduced by more than 50% under different paths compared with a traditional MPC, and the angle error is reduced by more than 70%. Meanwhile, the stability is increased by around 60%. The results show the feasibility and superiority of MPC based on ANFIS.