With the increasing demand for indoor positioning-based services, indoor positioning methods based on Bluetooth, Wi-Fi, ultra wide band (UWB), inertial navigation, and visual light communications (VLC) have been proposed. Considering the limitations of accuracy, cost and complexity, we propose a fusion positioning scheme integrating VLC positioning and inertial navigation base on particle filter. The experimental results demonstrated that the performance degradation caused by the multipath effect and light obstruction in VLC-based positioning and the cumulative error associated with inertial navigation are solved in the proposed fusion system. The accuracy of the fusion positioning system is in centimeters, which is two to four times better as compared to the VLC-based positioning or inertial navigation alone. Furthermore, the fusion positioning system has the advantages of high accuracy, energy saving, low cost, and easy to install, making it a promising candidate for future indoor positioning applications.

UWB and IMU fusion positioning methods have been widely concerned for its high accuracy and robustness in GNSS-denied environment. However, most of the existing methods are stay under the Markov assumption and ignore the credible estimation of the yaw angle. In this paper, we propose a novel tightly-coupled IMU and multiple UWB tags fusion framework based on a graph optimization model that is able to provide high precision positioning and attitude determination service, which provides a new technical approach for the practical application of UWB and IMU fusion. A relative rotation and motion state initialization algorithm is designed and presented, which solves the problems that IMU installation direction is difficult to measure and the initial state is not stationary in practical application. Simulation experiments shows the impact of UWB tags distribution distance and IMU noise parameters on system performance, which provides design reference for practical applications. Finally, the real-site experiment proves the positioning RMSE of 4 cm based on our fusion solution and these simulation evidences, which confirms the great performance of our fusion solution.

With the rapid development of smart technology, the need for location-based services (LBS) increases every day. Since classical positioning technology such as GPS cannot satisfy the needs of indoor positioning, new indoor positioning technologies, such as Bluetooth, Wi-Fi, and Visible light communication (VLC), have already cut a figure. VLC positioning has been proposed because it has higher accuracy, costs less, and is easier to accomplish in comparison to the other indoor positioning technologies. However, the practicality of VLC positioning is limited since it is easily affected by multipath effects and the layout of LEDs. Thus, we propose a fusion positioning system based on extended Kalman filters, which can fuse the VLC position and the inertial navigation data. The accuracy of the fusion positioning system is in centimeters, which is better compared to the VLC-based positioning or inertial navigation alone. Furthermore, the fusion positioning system has high accuracy, saves energy, costs little, and is easy to install, making it a promising candidate for future indoor positioning applications.

The relative pose estimation is one of the most fundamental components for multi-robot systems, while it still remains an open and challenging research topic in infrastructure-free environment. In this letter, we target improving the accuracy and robustness of relative pose estimation for ground robotic teams, and propose to fuse range and odometry measurements to estimate the relative pose using sliding window optimization. In the system, multiple UWB tags for ranging are equipped on each robot, and visual inertial odometry is applied for estimating the ego-motion pose for each robot. Aiming for simple and effective relative pose initialization, the triangulation uncertainty for multi-tag robots is analyzed, and an initialization method is designed. To cope with the complex environments such as continuous NLOS condition, a NLOS detection and range measurements filtering method is presented. We have conducted series of experiments to demonstrate the performance of the proposed approach.

Reservoir computing (RC) is an attractive paradigm of a recurrent neural network (RNN) architecture, owning to the ease of training and existing neuromorphic implementation. Its simulated performance matches other digital algorithms on a series of benchmarking tasks, such as prediction tasks and classification tasks. In this article, we propose a novel RC structure based on the coupled MEMS resonators with the enhanced dynamic richness to optimize the performance of the RC system both on the system level and data set level. Moreover, we first put forward that the dynamic richness of RC comprises linear dynamic richness and nonlinear dynamic richness, which can be enhanced by adding delayed feedbacks and nonlinear nodes, respectively. In order to set forth this point, we compare three typical RC structures, a single-nonlinearity RC structure with single-feedback, a single-nonlinearity RC structure with double-feedbacks, and the couple-nonlinearity RC structure with double-feedbacks. Specifically, four different tasks are enumerated to verify the performance of the three RC structures, and the results show the enhanced dynamic richness by adding delayed feedbacks and nonlinear nodes. These results prove that coupled MEMS resonators offer an interesting platform to implement a complex computing paradigm leveraging their rich dynamical features.

Nowadays, accurate and robust localization is preliminary for achieving a high autonomy for robots and emerging applications. More and more, sensors are fused to guarantee these requirements. A lot of related work has been developed, such as visual-inertial odometry (VIO). In this research, benefiting from the complementary sensing capabilities of IMU and cameras, many problems have been solved. However, few of them pay attention to the impact of different performance IMU on the accuracy of sensor fusion. When faced with actual scenarios, especially in the case of massive hardware deployment, there is the question of how to choose an IMU appropriately? In this paper, we chose six representative IMUs with different performances from consumer-grade to tactical grade for exploring. According to the final performance of VIO based on different IMUs in different scenarios, we analyzed the absolute trajectory error of Visual-Inertial Systems (VINS_Fusion). The assistance of IMU can improve the accuracy of multi-sensor fusion, but the improvement of fusion accuracy with different grade MEMS-IMU is not very significant in the eight experimental scenarios; the consumer-grade IMU can also have an excellent result. In addition, the IMU with low noise is more versatile and stable in various scenarios. The results build the route for the development of Inertial Navigation System (INS) fusion with visual odometry and at the same time, provide a guideline for the selection of IMU.