6 research outputs found
Clifford Algebra-Based Iterated Extended Kalman Filter with Application to Low-Cost INS/GNSS Navigation
The traditional GNSS-aided inertial navigation system (INS) usually exploits
the extended Kalman filter (EKF) for state estimation, and the initial attitude
accuracy is key to the filtering performance. To spare the reliance on the
initial attitude, this work generalizes the previously proposed trident
quaternion within the framework of Clifford algebra to represent the extended
pose, IMU biases and lever arms on the Lie group. Consequently, a
quasi-group-affine system is established for the low-cost INS/GNSS integrated
navigation system, and the right-error Clifford algebra-based EKF
(Clifford-RQEKF) is accordingly developed. The iterated filtering approach is
further applied to significantly improve the performances of the Clifford-RQEKF
and the previously proposed trident quaternion-based EKFs. Numerical
simulations and experiments show that all iterated filtering approaches fulfill
the fast and global convergence without the prior attitude information, whereas
the iterated Clifford-RQEKF performs much better than the others under
especially large IMU biases
Tightly coupled GNSS/INS integration via factor graph and aided by fish-eye camera
201911 bcrcAccepted ManuscriptPublishe
3D LiDAR Aided GNSS NLOS Mitigation for Reliable GNSS-RTK Positioning in Urban Canyons
GNSS and LiDAR odometry are complementary as they provide absolute and
relative positioning, respectively. Their integration in a loosely-coupled
manner is straightforward but is challenged in urban canyons due to the GNSS
signal reflections. Recent proposed 3D LiDAR-aided (3DLA) GNSS methods employ
the point cloud map to identify the non-line-of-sight (NLOS) reception of GNSS
signals. This facilitates the GNSS receiver to obtain improved urban
positioning but not achieve a sub-meter level. GNSS real-time kinematics (RTK)
uses carrier phase measurements to obtain decimeter-level positioning. In urban
areas, the GNSS RTK is not only challenged by multipath and NLOS-affected
measurement but also suffers from signal blockage by the building. The latter
will impose a challenge in solving the ambiguity within the carrier phase
measurements. In the other words, the model observability of the ambiguity
resolution (AR) is greatly decreased. This paper proposes to generate virtual
satellite (VS) measurements using the selected LiDAR landmarks from the
accumulated 3D point cloud maps (PCM). These LiDAR-PCM-made VS measurements are
tightly-coupled with GNSS pseudorange and carrier phase measurements. Thus, the
VS measurements can provide complementary constraints, meaning providing
low-elevation-angle measurements in the across-street directions. The
implementation is done using factor graph optimization to solve an accurate
float solution of the ambiguity before it is fed into LAMBDA. The effectiveness
of the proposed method has been validated by the evaluation conducted on our
recently open-sourced challenging dataset, UrbanNav. The result shows the fix
rate of the proposed 3DLA GNSS RTK is about 30% while the conventional GNSS-RTK
only achieves about 14%. In addition, the proposed method achieves sub-meter
positioning accuracy in most of the data collected in challenging urban areas
Seamless fusion: multi-modal localization for first responders in challenging environments
In dynamic and unpredictable environments, the precise localization of first responders and rescuers is crucial for effective incident response. This paper introduces a novel approach leveraging three complementary localization modalities: visual-based, Galileo-based, and inertial-based. Each modality contributes uniquely to the final Fusion tool, facilitating seamless indoor and outdoor localization, offering a robust and accurate localization solution without reliance on pre-existing infrastructure, essential for maintaining responder safety and optimizing operational effectiveness. The visual-based localization method utilizes an RGB camera coupled with a modified implementation of the ORB-SLAM2 method, enabling operation with or without prior area scanning. The Galileo-based localization method employs a lightweight prototype equipped with a high-accuracy GNSS receiver board, tailored to meet the specific needs of first responders. The inertial-based localization method utilizes sensor fusion, primarily leveraging smartphone inertial measurement units, to predict and adjust first responders’ positions incrementally, compensating for the GPS signal attenuation indoors. A comprehensive validation test involving various environmental conditions was carried out to demonstrate the efficacy of the proposed fused localization tool. Our results show that our proposed solution always provides a location regardless of the conditions (indoors, outdoors, etc.), with an overall mean error of 1.73 m