GPS/INS

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

Vision based navigation for an unmanned aerial vehicle

Abstract: We are developing a system for autonomous navigation of unmanned aerial vehicles (UAVs) based on computer vision. A UAV is equipped with on-board cameras and each UAV is provided with noisy estimates of its own state, coming from GPS/INS. The mission of the UAV is low altitude navigation from an initial position to a final position in a partially known 3-D environment while avoiding obstacles and minimizing path length. We use a hierarchical approach to path planning. We distinguish between a global offline computation, based on a coarse known model of the environment and a local online computation, based on the information coming from the vision system. A UAV builds and updates a virtual 3-D model of the surrounding environment by processing image sequences and fusing them with sensor data. Based on such a model the UAV will plan a path from its current position to the terminal point. It will then follow such path, getting more data from the on-board cameras, and refining map and local path in real time.

Robust localization using relative and absolute position estimates

Abstract: A low cost strategy based on well calibrated odometry is presented for localizing mobile robots The paper describes a two-step process for correction of 'systematic errors' in encoder measurements followed by fusion of the calibrated odometry with a gyroscope and GPS resulting in a robust localization scheme A Kalman filter operating on data from the sensors is used for estimating position and orientation of the robot Experimental results are presented that show an improvement of at least one order of magnitude in accuracy compared to the un-calibrated, un-filtered case Our method is systematic, simple and yields very good results We show that this strategy proves useful when the robot is using GPS to localize itself as well as when GPS becomes unavailable for some time As a result robot can move in and out of enclosed spaces, such as buildings, while keeping track of its position on the fly

Velocity/Position Integration Formula Part I: Application to In-Flight Coarse Alignment

Abstract: The in-flight alignment is a critical stage for airborne inertial navigation system/Global Positioning System (INS/GPS) applications. The alignment task is usually carried out by the Kalman filtering technique that necessitates a good initial attitude to obtain a satisfying performance. Due to the airborne dynamics, the in-flight alignment is much more difficult than the alignment on the ground. An optimization-based coarse alignment approach that uses GPS position/velocity as input, founded on the newly-derived velocity/position integration formulae is proposed. Simulation and flight test results show that, with the GPS lever arm well handled, it is potentially able to yield the initial heading up to 1 deg accuracy in 10 s. It can serve as a nice coarse in-flight alignment without any prior attitude information for the subsequent fine Kalman alignment. The approach can also be applied to other applications that require aligning the INS on the run.

Vehicle Sideslip and Roll Parameter Estimation using GPS

Abstract: This paper demonstrates a method for obtaining estimates of key vehicle states using the Global Positioning System (GPS) and Inertial Navigation System (INS) sensor measurements. A Kalman filter integrates the INS sensors with GPS to provide high update estimates of the sensor biases, heading, and vehicle velocities, which can be used to calculate the vehicle slip angle. Since the INS sensors and GPS antennas are attached to the vehicle body, roll and pitch effects from the vehicle motion and road influence the measurements obtained from the sensors and GPS. This paper develops a method that incorporates these roll and pitch effects to improve the accuracy of the vehicle state and sensor bias estimates. With accurate measurements of roll angle and roll rate, it is also possible to estimate key roll parameters, such as roll stiffness and damping ratio, with a second order dynamic model. Comparison of the vehicle state estimates with those predicted by the theoretical vehicle model yields similar results. This similarity verifies that the estimation scheme is giving appropriate estimates of the states.