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This paper aims to present a pipeline to progressively deal with the online external parameter calibration and estimator initialization of the Stereo-inertial measurement unit (IMU) system, which does not require any prior information and is suitable for system initialization in a variety of environments.

Before calibration and initialization, a modified stereo tracking method is adopted to obtain a motion pose, which provides prerequisites for the next three steps. Firstly, the authors align the pose obtained with the IMU measurements and linearly calculate the rough external parameters and gravity vector to provide initial values for the next optimization. Secondly, the authors fix the pose obtained by the vision and restore the external and inertial parameters of the system by optimizing the pre-integration of the IMU. Thirdly, the result of the previous step is used to perform visual-inertial joint optimization to further refine the external and inertial parameters.

The results of public data set experiments and actual experiments show that this method has better accuracy and robustness compared with the state of-the-art.

This method improves the accuracy of external parameters calibration and initialization and prevents the system from falling into a local minimum. Different from the traditional method of solving inertial navigation parameters separately, in this paper, all inertial navigation parameters are solved at one time, and the results of the previous step are used as the seed for the next optimization, and gradually solve the external inertial navigation parameters from coarse to fine, which avoids falling into a local minimum, reduces the number of iterations during optimization and improves the efficiency of the system.

This paper aims to describe a novel hybrid inertial measurement unit (IMU) for motion capturing via a new configuration of strategically distributed inertial sensors, and a calibration approach for the accelerometer and gyroscope sensors mounted in a flight vehicle motion tracker built on the inertial navigation system.

The hybrid-IMU is designed with five accelerometers and one auxiliary gyroscope instead of the accelerometer and gyroscope triads in the conventional IMU.

Simulation studies for tracking with both attitude angles and translational movement of a flight vehicle are conducted to illustrate the effectiveness of the proposed method.

The cross-quadratic terms of angular velocity are selected to process the direct measurements of angular velocities of body frame and to avoid the integration of angular acceleration vector compared with gyro-free configuration based on only accelerometers. The inertial sensors are selected from the commercial microelectromechanical system devices to realize its low-cost applications.

The selection of a suitable inertial measurement unit (IMU) is a critical step in an inertial navigation system (INS) design. Nevertheless, inertial sensors manufacturers are unwilling to publish their products’ accurate performance parameters along with a price. This paper aims to summarise the current IMU market review and point out parameters important for short-term inertial navigation.

The market review is based on the information published by manufacturers in brochures, datasheets and websites. Some information, including price, was also collected from sensors distributors. The entire collection of data includes data of over 150 sensors from 32 manufacturers and is valid for the first half of the year 2020.

This paper answers the following questions: •Why and where use inertial navigation? •Which parameters should one emphasise during IMU selection?•What is currently available on the IMU market? •Which parameters have a significant influence on price? •What are the advantages of specific sensor technology?

This paper gathers data published by IMU manufacturers, allowing for a quick overview of the current market. Based on real data, different sensor technologies are compared. The performed analysis presents the statistical basis for the IMU selection. By theoretical considerations a significance of sensor parameters is drawn and an approach to an IMU selection based on limited number of parameters is proposed. Although the considerations have been carried out regarding inertial navigation, the results from an extensive analysis of commercially available sensors may also be useful for other applications.

The aim of this work is to improve navigation solutions for the visually impaired, specifically problems with the heading given by the current GPS antennas. This heading is not reliable when the speed of the pedestrian or of the car is less than 10 km/h. The solution proposed is the use of one inertial measurement unit (IMU) coupled to a GPS, giving the navigation information in the way of heading and distance to the final destination.

The authors have developed their system using their IMU (compass, gyroscope and accelerometer) developed in the laboratory. They have also developed the user interface in one Smart Phone in the Android operating system coupled to the IMU using the Bluetooth transmission. Furthermore, the authors have tested their system in bad GPS reception conditions in Paris. They also used two other GPS systems (Navigon and Ariadne GPS) for testing the best way of giving the information: either “car navigation information: turn left or right at 100 meters … ” or “heading and distance to the final destination: destination at 2 o'clock, 150 meters”.

The main finding is that we can have a better way of navigation (for the visually impaired in pedestrian navigation) using one IMU, coupled to a compass and a GPS antenna in cities even in the case of hesitation of the user, using the information of “heading and distance to the final destination”.

This paper demonstrates how GPS coupled to an IMU can give a better way of navigating for the visually impaired; in especial using the information about heading and distance to the final destination.

Calibration and 6-DOF test of a unique inertial measurement unit (IMU) using a Quadrotor aircraft. The purpose of this paper is to discuss the above issue.

An IMU with the special capability of measuring the angular acceleration was developed and tested. A Quadrotor aircraft is used as 6-DOF test platform. Kinematics modeling of the Quadrotor was used in the determination of the Euler angles, while Dynamics modeling aided in the design the closed loop controller. For safety, the flight test was performed on a 6-DOF constrained reduced-gravity test stand.

The developed IMU is suitable for measuring states and its time derivatives of mini UAVs. Not only that but also a simple control algorithm can be integrated in the same processing unit (a 32 microcontroller in this case).

The tested IMU as well as the safety constrained test techniques are unique.

This study aims to present a visual-inertial simultaneous localization and mapping (SLAM) method for accurate positioning and navigation of mobile robots in the event of global positioning system (GPS) signal failure in buildings, trees and other obstacles.

In this framework, a feature extraction method distributes features on the image under texture-less scenes. The assumption of constant luminosity is improved, and the features are tracked by the optical flow to enhance the stability of the system. The camera data and inertial measurement unit data are tightly coupled to estimate the pose by nonlinear optimization.

The method is successfully performed on the mobile robot and steadily extracts the features on low texture environments and tracks features. The end-to-end error is 1.375 m with respect to the total length of 762 m. The authors achieve better relative pose error, scale and CPU load than ORB-SLAM2 on EuRoC data sets.

The main contribution of this study is the theoretical derivation and experimental application of a new visual-inertial SLAM method that has excellent accuracy and stability on weak texture scenes.

This paper aims to propose a method to solve the problem of localization and mapping of a two-wheeled inverted pendulum (TWIP) robot on the ground using the Stereo–inertial measurement unit (IMU) system. This method reparametrizes the pose according to the motion characteristics of TWIP and considers the impact of uneven ground on vision and IMU, which is more adaptable to the real environment.

When TWIP moves, it is constrained by the ground and swings back and forth to maintain balance. Therefore, the authors parameterize the robot pose as SE(2) pose plus pitch according to the motion characteristics of TWIP. However, the authors do not omit disturbances in other directions but perform error modeling, which is integrated into the visual constraints and IMU pre-integration constraints as an error term. Finally, the authors analyze the influence of the error term on the vision and IMU constraints during the optimization process. Compared to traditional algorithms, the algorithm is simpler and better adapt to the real environment.

The results of indoor and outdoor experiments show that, for the TWIP robot, the method has better positioning accuracy and robustness compared with the state-of-the-art.

The algorithm in this paper is proposed for the localization and mapping of a TWIP robot. Different from the traditional positioning method on SE(3), this paper parameterizes the robot pose as SE(2) pose plus pitch according to the motion of TWIP and the motion disturbances in other directions are integrated into visual constraints and IMU pre-integration constraints as error terms, which simplifies the optimization parameters, better adapts to the real environment and improves the accuracy of positioning.

The purpose of this paper is to introduce the development of an innovative mobile laser scanning (MLS) method for 3D indoor mapping. The generally accepted and used procedure for this type of mapping is usually performed using static terrestrial laser scanning (TLS) which is high-cost and time-consuming. Compared with conventional TLS, the developed method proposes a new idea with advantages of low-cost, high mobility and time saving on the implementation of a 3D indoor mapping.

This method integrates a low-cost 2D laser scanner with two indoor positioning techniques – ultra-wide band (UWB) and an inertial measurement unit (IMU), to implement a 3D MLS for reality captures from an experimental indoor environment through developed programming algorithms. In addition, a reference experiment by using conventional TLS was also conducted under the same conditions for scan result comparison to validate the feasibility of the developed method.

The findings include: preset UWB system integrated with a low-cost IMU can provide a reliable positioning method for indoor environment; scan results from a portable 2D laser scanner integrated with a motion trajectory from the IMU/UWB positioning approach is able to generate a 3D point cloud based in an indoor environment; and the limitations on hardware, accuracy, automation and the positioning approach are also summarized in this study.

As the main advantage of the developed method is low-cost, it may limit the automation of the method due to the consideration of the cost control. Robotic carriers and higher-performance 2D laser scanners can be applied to realize panoramic and higher-quality scan results for improvements of the method.

Moreover, during the practical application, the UWB system can be disturbed by variances of the indoor environment, which can affect the positioning accuracy in practice. More advanced algorithms are also needed to optimize the automatic data processing for reducing errors caused by manual operations.

The development of this MLS method provides a novel idea that integrates data from heterogeneous systems or sensors to realize a practical aim of indoor mapping, and meanwhile promote the current laser scanning technology to a lower-cost, more flexible, more portable and less time-consuming trend.

This paper aims to enable the unmanned aerial vehicles to inspect the surface condition of wind turbine in close range when the global positioning system signal is not reliable, and further improve its intelligence. So a visual-inertial odometry with point and line features is developed.

Visual front-end combining point and line features, as well as its purification strategies, are first presented to improve the robustness of feature tracking in low-textured scene and rapidity of segment detector. Additionally, the inertial measurement is integrated between keyframes as constrain to reduce tracking error existed in visual-only system. Second, the graph-based visual-inertial back-end is constructed. To parameterize line features effectively, the infinite line representation not sensitive to outdoor light is employed, in which Plücker and Cayley are selected for line re-projection and nonlinear optimization. Furthermore, Jacobians of the line re-projection errors are analytically derived for better accuracy.

Experiments are performed in various scenes of the wind farm. The results demonstrate that the tight-coupled visual-inertial odometry with point and line features is more precise on all the samples than conventional algorithms in complex wind farm environments. Additionally, the constructed line feature map can be used in the following research for autonomous navigation.

The proposed visual-inertial odometry works robustly in strong electromagnetic interference, low-textured and illumination-change wind farm.

This paper aims to present a multi-source information fusion algorithm based on factor graph for autonomous underwater vehicles (AUVs) navigation and positioning to address the asynchronous and heterogeneous problem of multiple sensors.

The factor graph is formulated by joint probability distribution function (pdf) random variables. All available measurements are processed into an optimal navigation solution by the message passing algorithm in the factor graph model. To further aid high-rate navigation solutions, the equivalent inertial measurement unit (IMU) factor is introduced to replace several consecutive IMU measurements in the factor graph model.

The proposed factor graph was demonstrated both in a simulated and vehicle environment using IMU, Doppler Velocity Log, terrain-aided navigation, magnetic compass pilot and depth meter sensors. Simulation results showed that the proposed factor graph processes all available measurements into the considerably improved navigation performance, computational efficiency and complexity compared with the un-simplified factor graph and the federal Kalman filtering methods. Semi-physical experiment results also verified the robustness and effectiveness.

The proposed factor graph scheme supported a plug and play capability to easily fuse asynchronous heterogeneous measurements information in AUV navigation systems.