Line Model-Based Drift Estimation Method for Indoor Monocular Localization

IRASSI is an interferometry-based mission concept, composed of five free-flying telescopes orbiting the Sun-Earth/Moon second Lagrange point, L2. IRASSI aims to observe particular regions of the sky, where Earth-like planets are likely to form, such as stellar disks and cold dust clouds. In these regions, important chemical and physical phenomena observable in the far-infrared frequencies (1 to 6 THz) can be recorded, enabling scientists to study star formation and evolution processes, as well as early planetary origins. Such phenomena must be resolved finely by the telescopes, with the angular resolution goal set to 0.1 arcsec or lower. To achieve such granular resolution magnitudes, the interferometer relies on dynamically changing baselines (i.e., physical separation) during the scientific observations. This is coupled with heterodyne detection, which collects and correlates detected signals of a celestial object from all telescopes simultaneously. With such a setup, the telescopes can combine their detected signals at different locations, improving the obtained angular resolution of the observations. In order to precisely correlate the detected far-infrared signals, the baseline vectors between the telescope reference points must be determined with an accuracy of 5 μm. This requirement poses a challenge to existing navigation technology. To overcome this challenge, a ranging system is employed to provide the distance measurements at micrometer-level accuracy between ranging sensors located at each satellite. A further important task is the determination of the lever arms (between the ranging sensor and the telescope reference point) and its orientation in space. Since the position estimation at L2 cannot be provided in absolute coordinates with such high accuracy, the navigation concept consists of two interacting components: (I) the absolute position estimation, with respect to Earth; and (II) the relative position estimation, determining satellite's positions, with respect to each other. For relative positioning, a geometric approach is currently under development, because it is capable of real-time processing. In future work, a semi-kinematic approach is aimed for, i.e., process dynamics is included, in order to increase robustness in case of measurement outliers or missing measurements. For absolute positioning, the Earth-based navigation method has been selected due to its potential to satisfy high accuracy expectations for the initialization of the relative position algorithms. The paper begins with a brief description of the IRASSI mission, where the issue of baseline accuracy determination is introduced. The Nominal Operations phase of the mission is presented in detail, with a focus on formation dynamics. A description of the navigation concepts, including the absolute and relative position estimation, is thereafter provided. The absolute and relative position estimation is analyzed in terms of their dynamic models, observation models and estimation methods. Furthermore, their performance relative to the requirements and overcoming challenges are discussed and compared in terms of their response time and accuracy. The key challenges in the frame of baseline measurement accuracy is then highlighted. Finally, the content of the paper is summarized and the scope of future work concludes the manuscript.

This work presents a hybrid method for navigation parameter estimation using sequential aerial images, where navigation parameters represent the position and velocity information of an aircraft for autonomous navigation. The proposed hybrid system is composed of two parts: relative position estimation and absolute position estimation. Computer simulation with two different sets of real aerial image sequences shows the effectiveness of the proposed hybrid parameter estimation algorithm.

This paper presents a novel adaptive iterated extended Kalman filter (AIEKF) for relative position and attitude estimation, taking into account the influence of model uncertainty. Considering a nonlinear stochastic discrete‐time system with unknown disturbance, the AIEKF algorithm adopts the Gauss‐Newton iterative optimization steps to implement a maximum a posteriori (MAP) estimation, and the switch‐mode combination technique is used to achieve the adaptive capability. The mean‐square estimation error (MSE) of the state estimate is derived. It is proved that the AIEKF can yield a smaller MSE than that of the traditional extended Kalman filter (EKF) or iterated extended Kalman filter (IEKF). The performance advantage of the AIEKF is illustrated via Monte Carlo simulations on a typical relative position and attitude estimation application. Through comparisons in different scenarios, the presented algorithm is shown to improve adaptability and ensure estimation accuracy.

In this paper, we describe the challenge of estimating the relative position between objects using a low-cost positioning device with a simple physical restriction. Also, we show the result of our experiment to examine the accuracy in estimation of the relative position.

Mobile Robot Vision Tracking System Using Dead Reckoning & Active Beacons

Relative position and attitude estimation for satellite formation with coupled translational and rotational dynamics

This paper proposes a method for estimating the speed and position of unsampled vehicles using sampled data from connected automated vehicles (CAVs). The determination of vehicle speed and position on the road is a challenging and crucial task, as they can effectively reflect traffic flow characteristics and contribute to traffic state estimation and intersection signal timing optimization. Connected automated vehicles have the capability to upload their own trajectory data while also capturing trajectory data of surrounding vehicles through onboard sensors. Therefore, this paper proposes a novel approach to estimate the speed and position of unsampled vehicles. Firstly, using real vehicle trajectory data, the correlation between the velocity of following vehicles and the velocity of leading vehicles under different densities is analyzed, leading to the development of a velocity estimation model incorporating a speed correction factor. Secondly, the correlation between time headway, the rate of change of following vehicle acceleration, and traffic density is examined. To address the issue of heterogeneous behavior in vehicle following described by the Intelligent Driver Model (IDM), a real-time optimization model for estimating vehicle position by optimizing IDM parameters is proposed. The velocity estimation model and the position estimation model are summarized as two nonlinear optimization problems. Finally, the proposed method is validated using actual vehicle trajectory data. Experimental results demonstrate that when the number of connected automated vehicles (CAVs) is 2, the proposed method reduces the average absolute error by 30.73% and the standard deviation of the average absolute error by 42.8% compared to a linear model-based speed estimation method under different density conditions. Compared to a method that estimates vehicle position by calibrating desired gaps, the proposed method reduces the average absolute error by 38.2% and the standard deviation of the average absolute error by 41.7% under different density conditions. Furthermore, the proposed method exhibits good practicality under different CAV penetration rates.

One of the problems faced by soccer robots is how to find out the position of the robot itself and other robots on the field. A simple way to find out the robot's position is to use the odometry method. However, odometry is weak in accumulating position errors that reduce the accuracy of the moving robot's absolute position estimation and orientation. This paper presents a robot position data estimation system that is to be implemented on the ERSOW wheeled soccer robot. The robot can determine its position based on a unique landmark: an L-shaped line on the soccer robot field. We use a deep neural network method to recognize landmark L-shaped. Vision systems and deep learning inferences run on the Robot Operating System platform. After obtaining the distance of the robot to the L-shaped landmark, the robot's orientation and position relative to the field can be accurately determined based on the omnidirectional camera's perception. The results of the position estimation system in this study can be used to reduce position errors resulting from the odometry method. Based on the landmark L-shape recognition test results conducted on 641 datasets, the validation accuracy value is 0.806. The results of testing the robot position generated by vision obtained the largest error x about 2.32 cm and y about 1.99 cm.

The relative position and attitude estimation of non-cooperative spacecraft is essential to high-precision guidance, navigation, and control systems. This paper mainly proposes a method to measure the relative position, velocity and attitude of non-cooperative spacecrafts using stereo camera and dynamics. First, using stereo camera observe the spacecraft, and extract the feature points of images. Then, calculate the 3D position of the feature points, and match the relationship between the feature points to obtain geometric constraint information between frames. Based on this, the relative position, velocity, attitude and angular velocity of the target spacecraft are calculated. According to relative orbit dynamics and relative attitude dynamics model, design extended Kalman filter to update the measurement results. The simulation results show that the estimation accuracy is better than 0.01m, the relative velocity accuracy is better than 0.001m/s, and the relative attitude accuracy is better than 0.01°. This method can provide technical support for the relative navigation of non-cooperative spacecraft.

Monocular Vision-based Two-stage Iterative Algorithm for Relative Position and Attitude Estimation of Docking Spacecraft

Study of Relative Position and Attitude Estimation and Control Scheme for the Final Phase of an Autonomous Docking Mission

Cubature Kalman filtering for relative spacecraft attitude and position estimation

In the context of underwater robotics, positioning and coordination of mobile agents can prove a challenging problem. To address this issue, we propose the use of electric sensing, with a technique inspired by weakly electric fishes. In particular, the approach relies on one or several of the agents applying an electric field to their environment. Using electric measures, others agents are able to reconstruct their relative position with respect to the emitter, over a range that is function of the geometry of the emitting agent and of the power applied to the environment. Efficacy of the technique is illustrated using a number of numerical examples. The approach is shown to allow coordination of unmanned underwater vehicles, including that of bio-inspired swimming robotic platforms.

Small Unmanned Aerial Systems (sUAS) operations are increasing in demand and complexity. Using multiple cooperative sUAS (i.e., a swarm) can be beneficial and is sometimes necessary to perform certain tasks (e.g. precision agriculture, mapping, surveillance) either independent or collaboratively. However, controlling the flight of multiple sUAS autonomously and in real-time in a challenging environment in terms of obstacles and navigation requires highly accurate absolute and relative position and velocity information for all platforms in the swarm. This information is also necessary to effectively and efficiently resolve possible collision encounters between the sUAS. In our swarm, each platform is equipped with a Global Navigation Satellite System (GNSS) sensor, an inertial measurement unit (IMU), a baro-altimeter and a relative range sensor (range radio). When GNSS is available, its measurements are tightly integrated with IMU and baro-altimeter measurements to obtain the platform's absolute position. At the same time, the raw measurements are exchanged with the other platforms to obtain a highly accurate relative position and velocity solution with integrity. In the presence of GNSS, this relative position and velocity is used to calibrate the range radios. When GNSS is not available due to external factors (e.g., obstructions, interference), the position and velocity estimators switch to an integrated solution based on IMU, baro and relative range measurements, to maintain an accurate relative position estimate, and reduce the drift in the swarm's absolute position estimate as is typical of an IMU-based system. Multiple multi-copter data collection platforms have been developed and equipped with GNSS, inertial sensors and range radios, which were developed at Ohio University. This paper outlines the underlying methodology, the platform hardware components and analyzes and discusses sUAS flight data results.

In this paper a novel approach is developed for relative state estimation of spacecraft flying in formation. The approach uses information from an optical sensor to provide multiple line-of-sight vectors from one spacecraft to another. The line-of-sight measurements are coupled with gyro measurements and dynamical models in an extended Kalman filter to determine relative attitude, position, and gyro biases. The quaternion is used to describe the relative kinematics, whereas general relative orbital equations are used to describe the positional dynamics. Three different attitude formulations are presented. The first estimates the relative attitude and individual gyro biases for the chief and deputy spacecraft. The second estimates the relative attitude, and the relative velocity bias and the deputy gyro bias. The third estimates the relative attitude, and the relative velocity bias and the chief gyro bias. Simulation results indicate that the combined sensor/estimator approach provides accurate relative attitude and position estimates.