Vision Navigation with Salient Features for Autonomous Aircraft

2013 ◽  
Vol 278-280 ◽  
pp. 1237-1241
Author(s):  
Jun Wei Yu ◽  
Nan Liu ◽  
Gui Cai Wang ◽  
Xiao Bo Jin
Keyword(s):  
Navigation System ◽  
Inertial Navigation System ◽  
Unscented Kalman Filter ◽  
Vision System ◽  
Control Points ◽  
Vision Navigation ◽  
State Information ◽  
Aircraft Navigation ◽  
Autonomous Aircraft ◽  
Sequential Images

A novel technique of vision-aided navigation for autonomous aircraft is presented in this paper. The aircraft’s position and pose are estimated with several control points. The saliency descriptor of corner is defined and the control points are selected according their saliency. Control points are tracked in sequential images based on Fourier-Melline transform. The unscented Kalman filter is used to fuse the aircraft state information provided by the vision system and the inertial navigation system. Experiments show that the accuracy, efficiency and robustness of aircraft navigation system are improved with the proposed method.

scholarly journals Design of Accurate Navigation System by Integrating INS and GPS modules

2019 ◽  
Vol 11 (4) ◽  
pp. 139-154
Author(s):  
M. RAJA ◽  
Gaurav ASTHANA ◽  
Ajay SINGH ◽  
Ashna SINGHAL ◽  
Pallavi LAKRA
Keyword(s):  
Navigation System ◽  
Inertial Navigation System ◽  
External Information ◽  
Navigation Systems ◽  
Final Solution ◽  
Long Term Stability ◽  
Noise Characteristics ◽  
Aircraft Navigation ◽  
Satellite Signal

Navigation has a huge application in aviation and aircraft automatic approach. Two widely used navigation systems are Global position System (GPS) and Inertial Navigation System (INS). Triangulation method used to determine the aircrafts location by GPS, speed whereas an INS, with the aid of gyroscope and accelerometer, estimates the location, velocity and alignment of an aircraft. Aircraft navigation is a complex task and using only one of the above navigation systems results in inaccurate and insufficient data. GPS stops working when satellite signal is not received, susceptible to interfere occasionally has high noise content, and has a low bandwidth, INS system requires external information for initialization has long-term drift errors. Certain errors like ionosphere interference, clock error, orbital error, position error, etc. might arise and disrupt the navigation process. In order to outrun the limitations of the above two systems and counter the errors, both INS and GPS can be integrated and used to attain more smooth, accurate and faster aircraft attitude estimates, as they have complementary strengths and limitations. GPS is stable for a long period and can act as an independent navigation system whereas INS is not susceptible to interference and signal losses has high radio bandwidth and works well for short intervals of time. In order to get accurate and precise attitude estimation, calculation of the parameters at different altitude using both systems is done; furthermore the comparison and contrast between the results is performed, measured quantities are transformed between various frames like longitudinal to rolling, calculation and elimination of errors is done producing the final solution. Because of integrated GPS and INS, the navigation system exhibits robustness, higher bandwidth, better noise characteristics, and long-term stability.

Application of Improved Unscented Kalman Filter to AUV Based on Terrain-Aided Strapdown Inertial Navigation System

2013 ◽  
Vol 389 ◽  
pp. 758-764 ◽  
Author(s):  
Qi Wang ◽  
Dong Li ◽  
Zi Jia Zhang ◽  
Chang Song Yang
Keyword(s):  
Kalman Filter ◽  
Information Fusion ◽  
Navigation System ◽  
Inertial Navigation System ◽  
Unscented Kalman Filter ◽  
Autonomous Underwater Vehicles ◽  
Inertial Navigation ◽  
Strapdown Inertial Navigation System ◽  
Integrated Navigation ◽  
Strapdown Inertial Navigation

To improve the navigation precision of autonomous underwater vehicles, a terrain-aided strapdown inertial navigation based on Improved Unscented Kalman Filter (IUKF) is proposed in this paper. The characteristics of strapdown inertial navigation system and terrain-aided navigation system are described in this paper, and improved UKF method is applied to the information fusion. Simulation experiments of novel integrated navigation system proposed in the paper were carried out comparing to the traditional Kalman filtering methods. The experiment results suggest that the IUKF method is able to greatly improve the long-time navigation precision, relative to the traditional information fusion method.

Towards an Optical Aircraft Navigation System

2014 ◽  
Vol 629 ◽  
pp. 321-326
Author(s):  
Javaan Chahl ◽  
Aakash Dawadee
Keyword(s):  
Optical Flow ◽  
Navigation System ◽  
High Performance ◽  
Vision System ◽  
Optical Navigation ◽  
Machine Vision System ◽  
Lateral Drift ◽  
Aircraft Navigation ◽  
Polarization Compass ◽  
Flow Detection

Navigation by means that are fully self contained, without the weight and cost of high performance inertial navigation units is highly desirable in many applications both military and civilian. In this paper we introduce a suite of sensors and behaviors that include: the means to reduce lateral drift due to wind using optical flow, detection of a constellation of landmarks using a machine vision system, and a polarization compass that is reliable at extreme latitudes based on polarization. In a series of flight trials and detailed simulations we have demonstrated that a combination of these functions achieves purely optical navigation with simplicity and robustness.

A fast alignment of marine strapdown inertial navigation system based on adaptive unscented Kalman Filter

2020 ◽  
pp. 014233122093429 ◽  
Author(s):  
Hossein Rahimi ◽  
Amir Ali Nikkhah ◽  
Kaveh Hooshmandi
Keyword(s):  
Kalman Filter ◽  
Navigation System ◽  
Inertial Navigation System ◽  
Unscented Kalman Filter ◽  
Inertial Navigation ◽  
Strapdown Inertial Navigation System ◽  
Initial Alignment ◽  
External Perturbations ◽  
Adaptive Unscented Kalman Filter ◽  
Strapdown Inertial Navigation

This study has presented an efficient adaptive unscented Kalman filter (AUKF) with the new measurement model for the strapdown inertial navigation system (SINS) to improve the initial alignment under the marine mooring conditions. Conventional methods of the accurate alignment in the ship’s SINS usually fail to succeed within an acceptable period of time due to the components of external perturbations caused by the movement of sea waves and wind waves. To speed up convergence, AUKF takes into account the impact of the dynamic acceleration on the filter and its gain adaptively tuned by considering the dynamic scale sensed by accelerometers. This approach considerably improved the corrections of the current residual error on the SINS and decreased the influence due to the external perturbations caused by the ship’s movement. Initial alignment algorithm based on AUKF is designed for large misalignment angles and verified by experimental data. The experimental test results show that the proposed algorithm enhanced the convergence speed of SINS initial alignment compared with some state-of-the-art existing approaches.

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