An RGB-D Camera Based Visual Positioning System for Assistive Navigation by a Robotic Navigation Aid

There are about 253 million people with visual impairment worldwide.Many of them use a white cane and/or a guide dog as the mobility tool for daily travel.Despite decades of efforts,electronic navigation aid that can replace white cane is still research in progress.In this paper,we propose an RGB-D camera based visual positioning system(VPS)for real-time localization of a robotic navigation aid(RNA)in an architectural floor plan for assistive navigation.The core of the system is the combination of a new 6-DOF depth-enhanced visual-inertial odometry(DVIO)method and a particle filter localization(PFL)method.DVIO estimates RNA’s pose by using the data from an RGB-D camera and an inertial measurement unit(IMU).It extracts the floor plane from the camera’s depth data and tightly couples the floor plane,the visual features(with and without depth data),and the IMU’s inertial data in a graph optimization framework to estimate the device’s 6-DOF pose.Due to the use of the floor plane and depth data from the RGB-D camera,DVIO has a better pose estimation accuracy than the conventional VIO method.To reduce the accumulated pose error of DVIO for navigation in a large indoor space,we developed the PFL method to locate RNA in the floor plan.PFL leverages geometric information of the architectural CAD drawing of an indoor space to further reduce the error of the DVIO-estimated pose.Based on VPS,an assistive navigation system is developed for the RNA prototype to assist a visually impaired person in navigating a large indoor space.Experimental results demonstrate that:1)DVIO method achieves better pose estimation accuracy than the state-of-the-art VIO method and performs real-time pose estimation(18 Hz pose update rate)on a UP Board computer;2)PFL reduces the DVIO-accrued pose error by 82.5%on average and allows for accurate wayfinding(endpoint position error≤45 cm)in large indoor spaces.

INVESTIGATION OF TIGHTLY COUPLED SINS/GPS INTEGRATION MIDCOURSE GUIDANCE FOR AIR-TO-AIR MISSILES

The applied problems of SINS/GPS integration navigation system existing in midcourse guidance of air to air missiles have been investigated recently. In comparison with those investigations existing in current publications, a new tightly coupled SINS/GPS integration navigation system for air to air missiles, based on the decorrelated pseudo range approach, is presented in this paper. Because of high jamming and dynamic of air to air missiles, inertial velocity aiding GPS receiver is used to provide a more accurate, jam resistant measurement for midcourse guidance systems. A tracking error estimator is designed to distinguish the correlation that existed between pseudo range measurements and inertial information. It is found better to regard inertial velocity aiding errors as the noise of which statistical properties are unknown. So using mixed Kalman/minimax filtering theory, one can obtain the new tracking error estimator with simple and robust algorithm through constructing a composite filter consisting of two parts: Kalman filter for the noise of known statistics and minimax filter for the unknown. In order to ensure this simple estimator stability, a new method is proposed to choose its parameters, based on Khargonekars work. Moreover, it is demonstrated that the given method also ensures the proposed estimator optimality. All the work mentioned above is involved in the tightly coupled SINS/GPS integration midcourse system design in which a set of low accuracy inertial components is shared by SINS and autopilot. Simulation results of a certain type of air to air missile are presented. Due to decorrelation by the tracking error estimator, only small white noise of pseudo range measurements remains. So it is shown that application of the new midcourse guidance system results in better guidance accuracy, higher jam resistance.

The Marginal Rao-Blackwellized Particle Filter for Mixed Linear/Nonlinear State Space Models

In this paper, the marginal Rao-Blackwellized particle filter （MRBPF）, which fuses the Rao-Blackwellized particle filter （RBPF） algorithm and the marginal particle filter （MPF） algorithm, is presented. The state space is divided into linear and non-linear parts, which can be estimated separately by the MPF and the optional Kalman filter. Through simulation in the terrain aided navigation （TAN） domain, it is demonstrated that, compared with the RBPF, the root mean square errors （RMSE） and the error variance of the nonlinear state estimations by the proposed MRBPF are respectively reduced by 29% and 96%, while the unique particle count is increased by 80%. It is also found that the MRBPF has better convergence properties, and analysis has shown that the existing RBPF is nothing more than a special case of the MRBPF.

Fast fine acquisition algorithm of GPS receiver aided by INS information

Acquisition time of global position system （GPS） receiver, which is the main factor contributes to time to first fix （TTFF）, can be shortened by estimating the Doppler frequency shift through external inertial navigation system （INS） information and almanac data and reducing the searching area. The traditional fast acquisition is analyzed, the fast acquisition of the GPS receiver aided is presented by INS information, and the signal is fine captured by spectrum zooming. Then the algorithm is simulated by sampled GPS intermediate frequency （IF） signal and the result verifies that this acquisition can dramatically improve the capability of GPS receiver and reduce its acquisition time.

TEC and Instrumental Bias Estimation of GAGAN Station Using Kalman Filter and SCORE Algorithm

The standalone Global Positioning System (GPS) does not meet the higher accuracy requirements needed for approach and landing phase of an aircraft. To meet the Category-I Precision Approach (CAT-I PA) requirements of civil aviation, satellite based augmentation system (SBAS) has been planned by various countries including USA, Europe, Japan and India. The Indian SBAS is named as GPS Aided Geo Augmented Navigation (GAGAN). The GAGAN network consists of several dual frequency GPS receivers located at various airports around the Indian subcontinent. The ionospheric delay, which is a function of the total electron content (TEC), is one of the main sources of error affecting GPS/SBAS accuracy. A dual frequency GPS receiver can be used to estimate the TEC. However, line-of-sight TEC derived from dual frequency GPS data is corrupted by the instrumental biases of the GPS receiver and satellites. The estimation of receiver instrumental bias is particularly important for obtaining accurate estimates of ionospheric delay. In this paper, two prominent techniques based on Kalman filter and Self-Calibration Of pseudo Range Error (SCORE) algorithm are used for estimation of instrumental biases. The estimated instrumental bias and TEC results for the GPS Aided Geo Augmented Navigation (GAGAN) station at Hyderabad (78.47°E, 17.45°N), India are presented.

Research on Airborne Terrain Matching System

In view of the airborne application characteristics such as flexible flight, large error of altimeter, large initial error of inertial navigation system, etc., a new terrain matching system architecture which is suitable for airborne application is presented. The key techniques in terrain matching system realizing process including system workflow, terrain matching algorithm and selection criterion of matching region are expatiated. The experimental results prove the rationality and feasibility of the proposed solution.

Neural Network Optimization of Multivariate KDE Bandwidth for Buoy Spatial Information

It is one of the responsibilities of the navigation support department to ensure the correct layout position of the light buoy and provide as accurate position information as possible for ship navigation and positioning.If the position deviation of the light buoy is too large to be detected in time,sending wrong navigation assistance information to the ship will directly affect the navigation safety of the ship and increase the pressure on the management department.Therefore,mastering the offset characteristics of light buoy is of great significance for the maintenance of light buoy and improving the navigation aid efficiency of light buoy.Kernel density estimation can intuitively express the spatial and temporal distribution characteristics of buoy position,and indicates the intensive areas of buoy position in the channel.In this paper,in order to speed up deciding the optimal variable width of kernel density estimator,an improved adaptive variable width kernel density estimator is proposed,which reduces the risk of too smooth probability density estimation phenomenon and improves the estimation accuracy of probability density.A fractional recurrent neural network is designed to search the optimal bandwidth of kernel density estimator.It not only achieves faster training speed,but also improves the estimation accuracy of probability density.

Underwater inertial error rectification with limited acoustic observations

Underwater inertial navigation is particularly difficult for the long-durance operations as many navigation systems such global satellite navigation systems are unavailable.The acoustic signal is a marvelous choice for underwater inertial error rectification due to its underwater penetration capability.However,the traditional Acoustic Positioning Systems(APS)are expensive and incapable of positioning with limited acoustic observations.Two novel underwater inertial error rectification algorithms with limited acoustic observations are proposed.The first one is the single acoustic-beacon Range-only Matching Aided Navigation(RMAN)method,which is inspired by matching navigation without reference maps and presented for the first time.The second is the improved single acoustic-beacon Virtual Long Baseline(VLBL)method,which considers the impact of indicated relative position increments on virtual beacon reconstruction.Both RMAN and improved VLBL are further developed when multi acoustic-beacons are available,named mAB-RMAN and mAB-VLBL.The comprehensive simulations and field investigations were conducted.The results demonstrated that the proposed methods achieved excellent accuracy and stability compared to the baseline,specifically,the mAB-RMAN and mAB-VLBL can reduce the inertial error by more than 90%and 98%when using single and double acoustic-beacons,respectively.These proposed techniques will provide new perspectives for underwater positioning,navigation,and timing.

Drag derived altitude aided navigation method

The navigation problem of the lifting reentry vehicles has attracted much research interest in the past decade. This paper researches the navigation in the blackout zone during the reentry phase of the aircraft, when the communication signals are attenuated and even interrupted by the blackout zone. However, when calculating altitude, a pure classic inertial navigation algorithm appears imprecise and divergent. In order to obtain a more precise aircraft altitude, this paper applies an integrated navigation method based on inertial navigation algorithms, which uses drag derived altitude to aid the inertial navigation during the blackout zone. This method can overcome the shortcomings of the inertial navigation system and improve the navigation accuracy. To further improve the navigation accuracy, the applicable condition and the main error factors, such as the atmospheric coefficient error and drag coefficient error are analyzed in detail. Then the damping circuit design of the navigation control system and the damping coefficients determination is introduced. The feasibility of the method is verified by the typical reentry trajectory simulation, and the influence of the iterative times on the accuracy is analyzed. Simulation results show that iterative three times achieves the best effect.