GPS/INS

About: GPS/INS is a research topic. Over the lifetime, 3554 publications have been published within this topic receiving 62784 citations.

An Autonomous Waist-Mounted Pedestrian Dead Reckoning System by Coupling Low-Cost MEMS Inertial Sensors and GPS Receiver for 3D Urban Navigation

Abstract: Global positioning system (GPS) offers a perfect solution to the 3-dimension(3D) navigation. However, the GPS-only solution can’t provide continuous and accurate position information in the unfavourable environments, such as urban canyons, indoor buildings, dense foliages due to signal blockage, interference, or jamming etc. A pedestrian dead reckoning (PDR) system integrating the self-contained inertial sensors with GPS receiver is proposed to provide a seamless outdoor/indoor 3D pedestrian navigation. The MEM sensor module attached to the user’s waist is composed of a 3-axis accelerometer, a 3-axis gyroscope, a 3-axis digital compass and a barometric pressure sensor, which doesn’t rely on any infrastructure. The positioning algorithm implements a loosely coupled GPS/PDR integration. The sensor data are fused via a complementary filter to reduce the integral drift and magnetic disturbance for accurate heading. The four key components of the PDR algorithm: step detection, stride length estimation, heading and position determination are described in detail and implemented by the microcontroller. The step is detected using the accelerometer signals by the combination of three approaches: sliding window, peak detection and zero-crossing. The step length is estimated using a simple linear relationship with the step frequency. By coupling the step length, azimuth and height, 3D navigation is achieved. The performance of the proposed system is carefully verified through several field outdoor and indoor walking tests. The positioning errors are below 3% of the total traveled distance. The main error source comes from the orientation estimation. The results indicate that the proposed system is effective in accurate tracking.

Sensor fusion in head pose tracking for augmented reality

Abstract: The focus of this thesis is on studying diverse techniques, methods and sensors for position and orientation determination with application to augmented reality applications. In Chapter 2 we reviewed a variety of existing techniques and systems for position determination. From a practical point of view, we discussed the need for a mobile system to localize itself while navigating through an environment. We identified two different localization instantiations, position tracking and global localization. In order to determine what information a mobile system has access to regarding its position, we discussed different sources of information and pointed out advantages and disadvantages. We concluded that due to the imperfections in actuators and sensors due to noise sources, a navigating mobile system should localize itself using information from different sensors. In Chapter 3, based on the analysis of the technologies presented in the Chapter 2 and the sensors described in this chapter, we selected a set of sensors from which to acquire and fuse the data in order to achieve the required robustness and accuracy. We selected for the inertial system three accelerometers (ADXL105) and three gyroscopes (Murata ENC05). To correct for gyro drift we use a TCM2 sensor that contains a two-axis inclinometer and a three-axes magnetometer (compass). Indoors we use a Firewire webcam to obtain the position and orientation information. Outdoors we use, in addition, a GPS receiver in combination with a radio data system (RDS) receiver to obtain DGPS correction information. Chapter 4 was concerned with development of inertial equations required for the navigation of a mobile system. To understand the effect of error propagation, the inertial equations were linearized. In this chapter we decompose the localization problem into attitude estimation and, subsequently, position estimation. We focus on obtaining a good attitude estimate without building a model of the vehicle dynamics. The dynamic model was replaced by gyro modeling. An Indirect (error state) Kalman filter that optimally incorporates inertial navigation and absolute measurements was developed for this purpose. The linear form of the system and measurement equations for the planar case derived here allowed us to examine the role of the Kalman filter as a signal processing unit. The extension of this formulation to the 3D case shows the same benefits. A tracking example in the 3D case was also shown in this chapter. Chapter 5 details all the necessary steps for implementing a vision positioning system. The pose tracking system for outdoor augmented reality is partly based on a vision system that tracks the head position within centimeters, the head orientation within degrees, and has an update rate of within a second. The algorithms that are necessary to obtain a robust vision system for tracking the automotion of a camera based on its observations of the physical world contain feature detection algorithms, camera calibration routines and pose determination algorithms. In Chapter 6 we summarize the presented work with concluding remarks. Here, we also present ideas and possibilities for future research. The conclusion is that since existing technology or sensor alone cannot solve the pose problem, we combine information from multiple sensors to obtain a more accurate and stable system. We present the development of an entire position determination system using off-the-shelve existing sensors integrated using separate Kalman filters. A unified solution is presented: inertial measurement integration for orientation and GPS in combination with a differential correction unit for positioning. PDF (corr1) uploaded: 28-5-2014

Single-axis rotation/azimuth-motion insulation inertial navigation control system with FOGs

Abstract: Rotation modulation technology could effectively improve the accuracy of the inertial navigation system (INS) by compensating for the biases of the inertial sensors automatically. However, the carrier angular motion and rotation control error could reduce the rotation modulation effect and then decrease the navigation accuracy. To address this problem, for the single-axis rotation INS, a novel rotation control scheme is presented. The control scheme employs the fiber optic gyros to control the inertial measurement unit (IMU) rotation angular velocity so that the INS with both rotation modulation and azimuth motion insulation functions. Furthermore, in order to reduce the control error, this study adopts two ways: optimizing the control strategy and shortening the delay time. The former way is to control the IMU rotating about the z-axis of the platform frame with respect to the navigation frame, rather than the up-axis of the navigation frame. The latter way is to apply interrupt mode rather than inquiry mode to complete the data transfer between the navigation and the control processors. The simulation and experimental results demonstrate that: the proposed method would not only realize the rotation modulation of the biases of the inertial sensors, but also achieve the insulation of the azimuth motion. The steady-state control error of the control system is less than 10” and the overshoot control error is less than 50”. Compared to the traditional SRINS, the navigation position error in the single-axis rotation/azimuth-motion insulation INS could reduce 50% in some navigation application.

Design and implementation of MIMU/GPS integrated navigation systems

Abstract: With recent research, we outline the framework of a MIMU/GPS integrated navigation system with Micro-programming Controlled Direct Memory Access (MCDMA) technique and decoupled state and bias estimation applied in. The micro-miniature inertial measurement unit (MIMU) sensor module in this system is composed of three QRS14 gyroscopes and three 3140 accelerometers which are mounted on the three orthogonal basal planes of a hexahedron. Two sets of GPS receivers are served in the GPS module so as to calculate the azimuth angle during the system initial alignment. The integrated system has both advantages of loose and tight coupling. For less computation, the decoupled state and bias estimation is applied. The constant but unknown biases of inertial components are estimated accordingly with successful simulation results.