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.

GPS/INS/Odometer Integrated System Using Fuzzy Neural Network for Land Vehicle Navigation Applications

Abstract: The integration of Global Positioning Systems (GPS) with Inertial Navigation Systems (INS) has been very actively studied and widely applied for many years. Some sensors and artificial intelligence methods have been applied to handle GPS outages in GPS/INS integrated navigation. However, the integrated system using the above method still results in seriously degraded navigation solutions over long GPS outages. To deal with the problem, this paper presents a GPS/INS/odometer integrated system using a fuzzy neural network (FNN) for land vehicle navigation applications. Provided that the measurement type of GPS and odometer is the same, the topology of a FNN used in a GPS/INS/odometer integrated system is constructed. The information from GPS, odometer and IMU is input into a FNN system for network training during signal availability, while the FNN model receives the observations from IMU and odometer to generate odometer velocity correction to enhance resolution accuracy over long GPS outages. An actual experiment was performed to validate the new algorithm. The results indicate that the proposed method can improve the position, velocity and attitude accuracy of the integrated system, especially the position parameters, over long GPS outages.

Analysis of image-based navigation system for rotorcraft low-altitude flight

Abstract: Some of the issues in the location of objects using a sequence of images from a passive sensor are examined. Image-object differential equations for a rotorcraft executing an arbitrary maneuver are developed. Assuming an onboard inertial navigation system for rotorcraft and state estimation, this study considers how object location is affected by the choice of Kalman filter estimation technique, the rotorcraft, and the object. Simulation results are presented. >

Simultaneous System Calibration of a Multi-LiDAR Multicamera Mobile Mapping Platform

Abstract: Mobile light detection and ranging (LiDAR) systems are widely used to generate precise 3-D spatial information, which in turn aids a variety of applications such as digital building model generation, transportation corridor asset management, telecommunications, precision agriculture, and infrastructure monitoring. Integrating such systems with one or more cameras would allow forward and backward projection between imagery and LiDAR data, thus facilitating several other high-level data processing activities, such as reliable feature extraction and colorization of point cloudsv. To increase the registration accuracy of point clouds derived from LiDAR data and imagery, along with their accuracy with respect to the ground truth, a simultaneous estimation of the mounting parameters relating the different laser scanners and cameras to the onboard global navigation satellite system (GNSS)/inertial navigation system (INS) unit is vital. This paper proposes a calibration procedure that directly estimates the mounting parameters for several spinning multibeam laser scanners and cameras onboard an airborne or terrestrial mobile platform. This procedure is based on the use of conjugate points and linear/planar features in overlapping images and point clouds derived from different drive-runs/flight lines. In order to increase the efficiency of semi-automatic conjugate feature extraction from the LiDAR data, specifically designed calibration boards covered by highly reflective surfaces that could be easily deployed and set up within an outdoor environment are used in this study. An experimental setup is used to evaluate the performance of the proposed calibration procedure for both airborne and terrestrial mobile mapping systems through the a posteriori variance factor of the least squares adjustment procedure and the quality of fit of the adjusted LiDAR point cloud and image points to linear/planar surfaces before and after the calibration process.

Particle filtering and Cramer-Rao lower bound for underwater navigation

Abstract: We have studied a sea navigation method relying on a digital underwater terrain map and sonar measurements. The method is applicable for both ships and underwater vessels. We have used experimental data to build an underwater map and to investigate the estimation performance. Since the problem is non-linear, due to the measurement relation, we apply a sequential Monte Carlo method, or particle filter, for the state estimation. The fundamental limitations in navigation uncertainty can be described in terms of the Cramer-Rao lower bound, which is interpreted in terms of the inertial navigation system (INS) error, the sensor accuracy and the terrain map excitation. Hence, the Cramer-Rao lower bound can be interpreted and used in the design for INS systems, sensor performance or, if these are given, how much terrain or depth excitation that is needed for use in positioning and navigation.