4,503 research outputs found
Safety-quantifiable Line Feature-based Monocular Visual Localization with 3D Prior Map
Accurate and safety-quantifiable localization is of great significance for
safety-critical autonomous systems, such as unmanned ground vehicles (UGV) and
unmanned aerial vehicles (UAV). The visual odometry-based method can provide
accurate positioning in a short period but is subjected to drift over time.
Moreover, the quantification of the safety of the localization solution (the
error is bounded by a certain value) is still a challenge. To fill the gaps,
this paper proposes a safety-quantifiable line feature-based visual
localization method with a prior map. The visual-inertial odometry provides a
high-frequency local pose estimation which serves as the initial guess for the
visual localization. By obtaining a visual line feature pair association, a
foot point-based constraint is proposed to construct the cost function between
the 2D lines extracted from the real-time image and the 3D lines extracted from
the high-precision prior 3D point cloud map. Moreover, a global navigation
satellite systems (GNSS) receiver autonomous integrity monitoring (RAIM)
inspired method is employed to quantify the safety of the derived localization
solution. Among that, an outlier rejection (also well-known as fault detection
and exclusion) strategy is employed via the weighted sum of squares residual
with a Chi-squared probability distribution. A protection level (PL) scheme
considering multiple outliers is derived and utilized to quantify the potential
error bound of the localization solution in both position and rotation domains.
The effectiveness of the proposed safety-quantifiable localization system is
verified using the datasets collected in the UAV indoor and UGV outdoor
environments
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
The Design a TDCP-Smoothed GNSS/Odometer Integration Scheme with Vehicular-Motion Constraint and Robust Regression
Global navigation satellite system (GNSS) is widely regarded as the primary positioning solution for intelligent transport system (ITS) applications. However, its performance could degrade, due to signal outages and faulty-signal contamination, including multipath and non-line-of-sight reception. Considering the limitation of the performance and computation loads in mass-produced automotive products, this research investigates the methods for enhancing GNSS-based solutions without significantly increasing the cost for vehicular navigation system. In this study, the measurement technique of the odometer in modern vehicle designs is selected to integrate the GNSS information, without using an inertial navigation system. Three techniques are implemented to improve positioning accuracy; (a) Time-differenced carrier phase (TDCP) based filter: A state-augmented extended Kalman filter is designed to incorporate TDCP measurements for maximizing the effectiveness of phase-smoothing; (b) odometer-aided constraints: The aiding measurement from odometer utilizing forward speed with the lateral constraint enhances the state estimation; the information based on vehicular motion, comprising the zero-velocity constraint, fault detection and exclusion, and dead reckoning, maintains the stability of the positioning solution; (c) robust regression: A weighted-least-square based robust regression as a measurement-quality assessment is applied to adjust the weightings of the measurements adaptively. Experimental results in a GNSS-challenging environment indicate that, based on the single-point-positioning mode with an automotive-grade receiver, the combination of the proposed methods presented a root-mean-square error of 2.51 m, 3.63 m, 1.63 m, and 1.95 m for the horizontal, vertical, forward, and lateral directions, with improvements of 35.1%, 49.6%, 45.3%, and 21.1%, respectively. The statistical analysis exhibits 97.3% state estimation result in the horizontal direction for the percentage of epochs that had errors of less than 5 m, presenting that after the intervention of proposed methods, the positioning performance can fulfill the requirements for road level applications.
Document type: Articl
Development of an Indoor Location Based Service Test Bed and Geographic Information System with a Wireless Sensor Network
In order to provide the seamless navigation and positioning services for indoor environments, an indoor location based service (LBS) test bed is developed to integrate the indoor positioning system and the indoor three-dimensional (3D) geographic information system (GIS). A wireless sensor network (WSN) is used in the developed indoor positioning system. Considering the power consumption, in this paper the ZigBee radio is used as the wireless protocol, and the received signal strength (RSS) fingerprinting positioning method is applied as the primary indoor positioning algorithm. The matching processes of the user location include the nearest neighbor (NN) algorithm, the K-weighted nearest neighbors (KWNN) algorithm, and the probabilistic approach. To enhance the positioning accuracy for the dynamic user, the particle filter is used to improve the positioning performance. As part of this research, a 3D indoor GIS is developed to be used with the indoor positioning system. This involved using the computer-aided design (CAD) software and the virtual reality markup language (VRML) to implement a prototype indoor LBS test bed. Thus, a rapid and practical procedure for constructing a 3D indoor GIS is proposed, and this GIS is easy to update and maintenance for users. The building of the Department of Aeronautics and Astronautics at National Cheng Kung University in Taiwan is used as an example to assess the performance of various algorithms for the indoor positioning system
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