Time-Relative RTK-GNSS: GNSS Loop Closure in Pose Graph Optimization
Chiba Institute of Technology（千叶工业大学）
Taro Suzuki
备注：
Published in IEEE Robotics and Automation Letters (RA-L) and presented at the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2020
发表信息：
IEEE Robotics and Automation Letters (RA-L), vol. 5, no. 3, pp. 4735-4742, July 2020
A pose-graph-based optimization technique is widely used to estimate robot poses using various sensor measurements from devices such as laser scanners and cameras. The global navigation satellite system (GNSS) has recently been used to estimate the absolute 3D position of outdoor mobile robots. However, since the accuracy of GNSS single-point positioning is only a few meters, the GNSS is not used for the loop closure of a pose graph. The main purpose of this study is to generate a loop closure of a pose graph using a time-relative real-time kinematic GNSS (TR-RTK-GNSS) technique. The proposed TR-RTK-GNSS technique uses time-differential carrier phase positioning, which is based on carrier-phase-based differential GNSS with a single GNSS receiver. Unlike a conventional RTK-GNSS, we can directly compute the robot's relative position using only a stand-alone GNSS receiver. The initial pose graph is generated from the accumulated velocity computed from GNSS Doppler measurements. To reduce the accumulated error of velocity, we use the TR-RTK-GNSS technique for the loop closure in the graph-based optimization framework. The kinematic positioning tests were performed using an unmanned aerial vehicle to confirm the effectiveness of the proposed technique. From the tests, we can estimate the vehicle's trajectory with approximately 3 cm accuracy using only a stand-alone GNSS receiver.
Ford AV, LLC（福特自动驾驶汽车有限责任公司）；Ford Motor Company（福特汽车公司）；Xona Space Systems（Xona空间系统公司）
There is a growing need for vehicle positioning information to support Advanced Driver Assistance Systems (ADAS), Connectivity (V2X), and Autonomous Driving (AD) features. These range from a need for road determination ($<$5 meters), lane determination ($<$1.5 meters), and determining where the vehicle is within the lane ($<$0.3 meters). This paper presents the Ford Highway Driving RTK (Ford-HDR) dataset. This dataset includes nearly 30,000 km of data collected primarily on North American highways during a driving campaign designed to validate driver assistance features in 2018. This includes …
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. I…
Unmanned aerial vehicles (UAVs) are becoming largely ubiquitous with an increasing demand for aerial data. Accurate navigation and localization, required for precise data collection in many industrial applications, often relies on RTK GNSS. These systems, able of centimeter-level accuracy, require a setup and calibration process and are relatively expensive. This paper addresses the problem of accurate positioning and navigation of UAVs through cooperative localization. Inexpensive ultra-wideband (UWB) transceivers installed on both the UAV and a support ground robot enable centimeter-level re…
Estimation of articulated angle in six-wheeled dump trucks using multiple GNSS receivers for autonomous driving
Chiba Institute of Technology（千叶工业大学）；Tohoku University（东北大学）；Kowatech Co., Ltd.（Kowatech有限公司）；Sato Koumuten Co., Ltd.（佐藤工务店有限公司）；Sanyo-Technics Co., Ltd.（三洋技术有限公司）；The University of Tokyo（东京大学）
We present a robust and precise localization system that achieves centimeter-level localization accuracy in disparate city scenes. Our system adaptively uses information from complementary sensors such as GNSS, LiDAR, and IMU to achieve high localization accuracy and resilience in challenging scenes, such as urban downtown, highways, and tunnels. Rather than relying only on LiDAR intensity or 3D geometry, we make innovative use of LiDAR intensity and altitude cues to significantly improve localization system accuracy and robustness. Our GNSS RTK module utilizes the help of the multi-sensor fus…
Global navigation satellite systems (GNSS) are one of the utterly popular sources for providing globally referenced positioning for autonomous systems. However, the performance of the GNSS positioning is significantly challenged in urban canyons, due to the signal reflection and blockage from buildings. Given the fact that the GNSS measurements are highly environmentally dependent and time-correlated, the conventional filtering-based method for GNSS positioning cannot simultaneously explore the time-correlation among historical measurements. As a result, the filtering-based estimator is sensit…
This paper presents the most thorough study to date of vehicular carrier-phase differential GNSS (CDGNSS) positioning performance in a deep urban setting unaided by complementary sensors. Using data captured during approximately 2 hours of driving in and around the dense urban center of Austin, TX, a CDGNSS system is demonstrated to achieve 17-cm-accurate 3D urban positioning (95% probability) with solution availability greater than 87%. The results are achieved without any aiding by inertial, electro-optical, or odometry sensors. Development and evaluation of the unaided GNSS-based precise po…
University of Turku（图尔库大学）；École Polytechnique Fédérale de Lausanne (EPFL)（洛桑联邦理工学院（EPFL））
Small unmanned aerial vehicles (UAV) have penetrated multiple domains over the past years. In GNSS-denied or indoor environments, aerial robots require a robust and stable localization system, often with external feedback, in order to fly safely. Motion capture systems are typically utilized indoors when accurate localization is needed. However, these systems are expensive and most require a fixed setup. Recently, visual-inertial odometry and similar methods have advanced to a point where autonomous UAVs can rely on them for localization. The main limitation in this case comes from the environ…
A Ground Mobile Robot for Autonomous Terrestrial Laser Scanning-Based Field Phenotyping
School of Electrical and Computer Engineering（电气与计算机工程学院）；The University of Georgia（佐治亚大学）；Bio-Sensing, Automation and Intelligence Laboratory（生物传感、自动化与智能实验室）；Agricultural & Biological Engineering（农业与生物工程）；The University of Florida（佛罗里达大学）
Robust, high-precision global localization is fundamental to a wide range of outdoor robotics applications. Conventional fusion methods use low-accuracy pseudorange based GNSS measurements ($>>5m$ errors) and can only yield a coarse registration to the global earth-centered-earth-fixed (ECEF) frame. In this paper, we leverage high-precision GNSS carrier-phase positioning and aid it with local visual-inertial odometry (VIO) tracking using an extended Kalman filter (EKF) framework that better resolves the integer ambiguity concerned with GNSS carrier-phase. %to achieve centimeter-level accuracy …
CIFASIS, French Argentine International Center for Information and Systems Sciences (CONICET-UNR)（法阿国际信息与系统科学中心（国家科学技术研究委员会-罗萨里奥国立大学））；University of Zaragoza（萨拉戈萨大学）
Estimation techniques to precisely localize a kinematic platform with GNSS observables can be broadly partitioned into two categories: differential, or undifferenced. The differential techniques (e.g., real-time kinematic (RTK)) have several attractive properties, such as correlated error mitigation and fast convergence; however, to support a differential processing scheme, an infrastructure of reference stations within a proximity of the platform must be in place to construct observation corrections. This infrastructure requirement makes differential processing techniques infeasible in many l…
