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.

Stroke and related complications such as hemiplegia and disability create huge burdens for human society in the 21st century, which leads to a great need for rehabilitation and daily life assistance. To address this issue, continuous efforts are devoted in human–machine interaction (HMI) technology, which aims to capture and recognize users’ intentions and fulfil their needs via physical response. Based on the physiological structure of the human hand, a dimension-adjustable linkage-driven hand exoskeleton with 10 active degrees of freedom (DoFs) and 3 passive DoFs is proposed in this study, which grants high-level synergy with the human hand. Considering the weight of the adopted linkage design, the hand exoskeleton can be mounted on the existing up-limb exoskeleton system, which greatly diminishes the burden for users. Three rehabilitation/daily life assistance modes are developed (namely, robot-in-charge, therapist-in-charge, and patient-in-charge modes) to meet specific personal needs. To realize HMI, a thin-film force sensor matrix and Inertial Measurement Units (IMUs) are installed in both the hand exoskeleton and the corresponding controller. Outstanding sensor–machine synergy is confirmed by trigger rate evaluation, Kernel Density Estimation (KDE), and a confusion matrix. To recognize user intention, a genetic algorithm (GA) is applied to search for the optimal hyperparameters of a 1D Convolutional Neural Network (CNN), and the average intention-recognition accuracy for the eight actions/gestures examined reaches 97.1% (based on K-fold cross-validation). The hand exoskeleton system provides the possibility for people with limited exercise ability to conduct self-rehabilitation and complex daily activities.

As emerging pollutants of concern, microplastics (MPs) have been found in different water environments and have an impact on human health through the aquatic food chain. To advance our understanding of the traceability and environmental fate of MPs, reproducible and accurate methods, techniques, and analytical methods are necessary for MP type identification and characterization. In this study, based on Raman spectroscopy technology to extract characteristic peak information of MPs with fingerprint features, coupled to sparse autoencoder (SAE) and softmax classifier framework, the rapid identification and classification of six common MP (PET, PVC, PP, PS, PC, PE) particles in five water (pure water, rain water, lake water, tap water, and sea water) environments was realized. The results show that the average test accuracy of the trained algorithm is as high as 99.1%, which is better than 93.95% and 74.55% of the classical machine learning algorithms support vector machine (SVM) and back propagation (BP) neural network. Success rate indicates that the proposed method can be used to identify the MP samples.