Inertial navigation system

About: Inertial navigation system is a research topic. Over the lifetime, 14582 publications have been published within this topic receiving 190618 citations. The topic is also known as: intertial guidance system & inertial reference platform.

Self-contained mapping and positioning system utilizing point cloud data

Abstract: A self-contained mapping and positioning system for underground mining is provided that is capable of mapping the topography of a region, such as a mine tunnel, and further being able to use the mapped data to determine the position of an object, such as a mining vehicle, within the mine tunnel. The system includes an inertial navigation system (12), a central processing unit (14), a three-dimensional database (16), a three-dimensional camera system (18), an operator console (20) and a survey system having a three-dimensional laser scanner. The survey system using the three-dimensional laser scanner produces point cloud data, i.e., a set of data points representing the topography of the region. The point cloud data is stored within a storage device until the entire region is mapped and then transmitted to the operator console to be post processed. After post processing, the data is exported to the three-dimensional database (16) and then indexed for ease of use by the central processing unit. To determine the position of the object within the region, the system of the instant invention initializes the object in its current position. The object is then either remotely or directly guided to another position from the current position where it is brought to an estimated position by the inertial navigation system (12) or is remotely controlled for performing work, e.g., drilling a mine heading. After a predetermined time of moving the object, there is an error in the position of the object and the operator console makes a call to the central processing unit (14) on board the object to automatically calculate the true position based on the point cloud data stored within the database to update the position of the object. This is done by approximating a search range for the database (16) according to the estimated position. A subset of data corresponding to the search range is removed from the database (16). The three-dimensional camera system then scans images of the surface in proximity to the object and converts the images to a patch of point cloud data. The patch of point cloud data is then matched against the subset of data corresponding to the search range of point cloud data removed from the three-dimensional database (16) until there is less than a predetermined minimum error distance. At that point, the true position of the object is known. This new position is then put back into the inertial navigation system (12) and the positional data of the object is updated with the correct positional data.

Rapid Fine Strapdown INS Alignment Method under Marine Mooring Condition

Abstract: In order to solve the strapdown inertial navigation system (INS) alignment problem under the marine mooring condition, the rapid strapdown INS fine alignment method is proposed. This method uses the gravity in the inertial frame to deal with the lineal and angular disturbances. Also the forward and backward processes for strapdown INS calculation and filter estimation are designed. Making use of the forward and backward processes to repeatedly process the saved inertial measurement unit (IMU) data sequence could quickly obtain the initial strapdown attitude matrix. Simulation and trial test validate the performance of the proposed rapid fine strapdown INS alignment.

Quaternion-based unscented Kalman filter for accurate indoor heading estimation using wearable multi-sensor system.

Abstract: Inertial navigation based on micro-electromechanical system (MEMS) inertial measurement units (IMUs) has attracted numerous researchers due to its high reliability and independence. The heading estimation, as one of the most important parts of inertial navigation, has been a research focus in this field. Heading estimation using magnetometers is perturbed by magnetic disturbances, such as indoor concrete structures and electronic equipment. The MEMS gyroscope is also used for heading estimation. However, the accuracy of gyroscope is unreliable with time. In this paper, a wearable multi-sensor system has been designed to obtain the high-accuracy indoor heading estimation, according to a quaternion-based unscented Kalman filter (UKF) algorithm. The proposed multi-sensor system including one three-axis accelerometer, three single-axis gyroscopes, one three-axis magnetometer and one microprocessor minimizes the size and cost. The wearable multi-sensor system was fixed on waist of pedestrian and the quadrotor unmanned aerial vehicle (UAV) for heading estimation experiments in our college building. The results show that the mean heading estimation errors are less 10° and 5° to multi-sensor system fixed on waist of pedestrian and the quadrotor UAV, respectively, compared to the reference path.

Modeling and Calibration of Inertial and Vision Sensors

Abstract: This paper is concerned with the problem of estimating the relative translation and orientation of an inertial measurement unit and a camera, which are rigidly connected. The key is to realize that this problem is in fact an instance of a standard problem within the area of system identification, referred to as a gray-box problem. We propose a new algorithm for estimating the relative translation and orientation, which does not require any additional hardware, except a piece of paper with a checkerboard pattern on it. The method is based on a physical model which can also be used in solving, for example, sensor fusion problems. The experimental results show that the method works well in practice, both for perspective and spherical cameras.

Kalman-Filtering-Based In-Motion Coarse Alignment for Odometer-Aided SINS

Abstract: In this paper, the in-motion coarse alignment (IMCA) for odometer-aided strap-down inertial navigation system (SINS) is investigated with the main focus on compensating for the dynamic errors of gyroscope induced by severe maneuvering. A new Kalman-filtering-based IMCA method for an odometer-aided SINS is presented. A novel closed-loop approach to estimating the attitude matrix from the current body frame to the initial body frame is proposed, in which the attitude error between the closed-loop calculation and the true attitude matrix is first estimated, and then, the estimated attitude matrix is obtained by refining the closed-loop calculation with the estimated attitude error. A linear state-space model for the attitude error is derived, and then, a Kalman filter is employed to track the attitude error. Experimental results illustrate that the proposed closed-loop approach can estimate the attitude matrix from the current body frame to the initial body frame better than the existing open-loop approach, which results in improved alignment accuracy as compared with the existing optimization-based alignment method for the odometer-aided SINS when the vehicle maneuvers severely.