ION Alberta Virtual Meeting – Thursday November 25, 2021

Three Unversity of Calgary Student Papers from ION GNSS+ 2021 were presented. ION GNSS+ is the worlds largest technical meeting on GNSS positioning and technology. Each year the University of Calgary sends multiple students who present award winning research on their respective topics.

1. Radar-Based Localization Using Visual Feature Matching

Speaker: Mohamed Elkholy received a B.Sc. degree in Civil Engineering (2011) and M.Sc. degree in Civil Engineering (2018) from Alexandria University, Alexandria, Egypt. He is currently a Ph.D. candidate at the Geomatics Department, the University of Calgary, Calgary, AB, Canada. His current research focuses on Using Radar to aid IMU to enhance the navigation solution in all environment.

Abstract: In unmanned ground vehicles (UGV), the global navigation satellite system (GNSS) is the primary sensor used to estimate the vehicle’s position. However, the GNSS signal suffers from blockage or multipath error, especially when driving through canyons or tunnels or beside high buildings, e.g., downtown areas. Thus, frequency modulated continuous wave (FMCW) 360o Radar is used to compensate for the GNSS outage. This paper proposes a novel approach based on the Oriented FAST and Rotated BRIEF (ORB) method to detect features from Radar scans and matching them to estimate the vehicle’s pose. The experimental work proved the efficiency of the proposed method over the traditional techniques, e.g., Iterative Closest Point (ICP), Normal Distribution Transform (NDT), and other traditional visual features detecting methods, e.g., SURF and FAST.

2. Precise and Continuous Attitude Estimation Using Single-Antenna GNSS/MEMS MARG Sensor Integrated System

Speaker: Wei Ding received his bachelor’s and master’s degrees in Geomatics engineering from Liaoning Technical University, Fuxin, China, in 2015 and 2018, respectively. He is currently a PhD candidate with the Department of Geomatics Engineering, Schulich School of Engineering, University of Calgary, Calgary, AB, Canada. His research interests include inertial navigation, sensor fusion, and attitude estimation.

Abstract: Time-differenced carrier phases (TDCPs) of a single-antenna GNSS receiver can be applied to estimate the pitch and heading angles of vehicle. But there are some limitations: 1) the poor system performance in static or low dynamics; 2) the system is lack of continuity and even fails if observed satellites are not enough; 3) the system is unable to estimate the roll angle. We proposed a novel attitude estimation algorithm using a single-antenna GNSS receiver integrated with a MEMS magnetic, angular rate, and gravity (MARG) sensor to address these problems. An error state Kalman filter is designed to fuse GNSS and MARG data to provide precise and continuous attitude estimates. The state vector consists of the attitude error and gyro bias variation to be consistent with attitude estimation using only a MARG sensor. Instead of deriving the design matrix using rotation matrix which would result in complicated equations, the measurement model relates the GNSS derived pitch and heading to the error state based on the error quaternion theory, which can give a succinct design matrix. As a result, the single-antenna GNSS/MEMS MARG sensor integrated system output GNSS and MARG fused navigation solutions in high dynamics while the system can provide continuous navigation solutions with the MARG sensor in stationary or low dynamics. The results of a dynamic test show that the proposed method is capable of providing precise and continuous 3D attitude solutions with better performance under all conditions when compared to GNSS or MARG sensor alone systems.

3. SIR Particle Filter in Float Solution for Ambiguity Resolution

Speaker: Rene Manzano-Islas Rene Manzano is a PhD student in the Department of Geomatics Engineering at the University of Calgary. His research is about the performance assessment of non-linear sampling estimation methods such as the Particle Filter carrier phase GNSS applications.

Abstract: In this paper, we implement a Sequential Importance Resampling (SIR) Particle Filter (PF) for estimating the full geometry-basedfloat solution state vector for Global Navigation Satellite System (GNSS) ambiguity resolution. This PF estimates the user position, velocity and acceleration states, as well as the float ambiguities using L1 GPS carrier phase and pseudorange observations. We estimate an empirical covariance matrix Pk from the distribution of the particles after resampling based on the incorporated measurements of each epoch. This will allow the particle distribution to be transformed using the integer decorrelating Z transformation of the LAMBDA method. We assess the performance of a float solution based on point mass representation compared to the typically used Extended Kalman Filter (EKF) for searching the integer ambiguities using the three common search methods: Integer Rounding, Integer Bootstrapping and Integer Least Squares with and without an application of the Z transformation.