Mobile robot localization with GNSS multipath detection using pseudorange residuals
Future Robotics Technology Center, Chiba Institute of Technology（千叶工业大学未来机器人技术中心）
Taro Suzuki
备注：
This is an electronic version of an article published in ADVANCED ROBOTICS, 33(12):602-613, 2019. ADVANCED ROBOTICS is available online at: www.tandfonline.com/Article DOI: 10.1080/01691864.2019.1619622
This paper proposes a novel positioning technique suitable for use in mobile robots in urban environments in which large global navigation satellite system (GNSS) positioning errors occur because of multipath signals. During GNSS positioning, the GNSS satellites that are obstructed by buildings emit reflection and diffraction signals, which are called non-line-of-sight (NLOS) multipath signals. These multipath signals cause major positioning errors. The key concept considered in this paper is the estimation of a user's position using the likelihood of the position hypotheses computed from the GNSS pseudoranges, consisting only of LOS signals based on the analysis of the pseudorange residuals. To determine the NLOS GNSS signals from the pseudorange residuals at the user's position, it is necessary to accurately determine the position before the computation of the pseudorange residuals. This problem is solved using a particle filter. We propose a likelihood estimation method using the Mahalanobis distance between the hypotheses of the user's position computed from only the LOS pseudoranges and the particles. To confirm the effectiveness of the proposed technique, a positioning test was performed in a real-world urban environment. The results demonstrated that the proposed method is effective for accurately estimating the user's position in urban canyons.
Absolute positioning is an essential factor for the arrival of autonomous driving. Global Navigation Satellites System (GNSS) receiver provides absolute localization for it. GNSS solution can provide satisfactory positioning in open or sub-urban areas, however, its performance suffered in super-urbanized area due to the phenomenon which are well-known as multipath effects and NLOS receptions. The effects dominate GNSS positioning performance in the area. The recent proposed 3D map aided (3DMA) GNSS can mitigate most of the multipath effects and NLOS receptions caused by buildings based on 3D c…
In this paper, we propose a 3D LiDAR aided global navigation satellite system (GNSS) non-line-of-sight (NLOS) mitigation method caused by both static buildings and dynamic objects. A sliding window map describing the surrounding of the ego-vehicle is first generated, based on real-time 3D point clouds from a 3D LiDAR sensor. Then, NLOS receptions are detected based on the sliding window map using a proposed fast searching method which is free of the initial guess of the position of the GNSS receiver. Instead of directly excluding the detected NLOS satellites from further positioning estimation…
LF-GNSS: Towards More Robust Satellite Positioning with a Hard Example Mining Enhanced Learning-Filtering Deep Fusion Framework
Department of Precision Instrument and the State Key Laboratory of Precision Space-Time Information Sensing Technology, Tsinghua University, Beijing, 100084, China（精密仪器系和精密时空信息感知技术国家重点实验室，清华大学，北京，100084，中国）
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…
One of the major errors affecting GNSS signals in urban canyons is GNSS multipath error. In this work, we develop a Gazebo plugin which utilizes a ray tracing technique to account for multipath effects in a virtual urban canyon environment using virtual satellites. This software plugin balances accuracy and computational complexity to run the simulation in real-time for both software-in-the-loop (SITL) and hardware-in-the-loop (HITL) testing. We also construct a 3D virtual environment of Hong Kong and compare the results from our plugin with the GNSS data in the publicly available Urban-Nav da…
The possible applications and benefits of autonomous Unmanned Aerial Vehicle (UAV) use in urban areas are gaining considerable attention. Before these possibilities can be realized, it is essential that UAVs be able to navigate reliably and precisely in urban environments. The most common means of determining the location of a UAV is to utilize position measurements from Global Navigation Satellite Systems (GNSS). In urban environments, however, GNSS measurements are significantly degraded due to occlusions and multipath. This research analyzes the use of camera Line-of-Sight (LOS) measurement…
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 …
This paper proposes an improved global navigation satellite system (GNSS) positioning method that explores the time correlation between consecutive epochs of the code and carrier phase measurements which significantly increases the robustness against outlier measurements. Instead of relying on the time difference carrier phase (TDCP) which only considers two neighboring epochs using an extended Kalman filter (EKF) estimator, this paper proposed to employ the carrier-phase measurements inside a window, the so-called window carrier-phase (WCP), to constrain the states inside a factor graph. A le…
Hong Kong Polytechnic University（香港理工大学）；University of California, Berkeley（加州大学伯克利分校）
Mapping and localization is a critical module of autonomous driving, and significant achievements have been reached in this field. Beyond Global Navigation Satellite System (GNSS), research in point cloud registration, visual feature matching, and inertia navigation has greatly enhanced the accuracy and robustness of mapping and localization in different scenarios. However, highly urbanized scenes are still challenging: LIDAR- and camera-based methods perform poorly with numerous dynamic objects; the GNSS-based solutions experience signal loss and multipath problems; the inertia measurement un…
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…
Accurate and globally referenced global navigation satellite system (GNSS) based vehicular positioning can be achieved in outlier-free open areas. However, the performance of GNSS can be significantly degraded by outlier measurements, such as multipath effects and non-line-of-sight (NLOS) receptions arising from signal reflections of buildings. Inspired by the advantage of batch historical data in resisting outlier measurements, in this paper, we propose a graduated non-convexity factor graph optimization (FGO-GNC) to improve the GNSS positioning performance, where the impact of GNSS outliers …
For reliable operation on urban roads, navigation using the Global Navigation Satellite System (GNSS) requires both accurately estimating the positioning detail from GNSS pseudorange measurements and determining when the estimated position is safe to use, or available. However, multiple GNSS measurements in urban environments contain biases, or faults, due to signal reflection and blockage from nearby buildings which are difficult to mitigate for estimating the position and availability. This paper proposes a novel particle filter-based framework that employs a Gaussian Mixture Model (GMM) lik…
Robust navigation in urban environments has received a considerable amount of both academic and commercial interest over recent years. This is primarily due to large commercial organizations such as Google and Uber stepping into the autonomous navigation market. Most of this research has shied away from Global Navigation Satellite System (GNSS) based navigation. The aversion to utilizing GNSS data is due to the degraded nature of the data in urban environment (e.g., multipath, poor satellite visibility). The degradation of the GNSS data in urban environments makes it such that traditional (GNS…
Adaptive Factor Graph-Based Tightly Coupled GNSS/IMU Fusion for Robust Positionin
School of Technology and Innovations, University of Vaasa（瓦萨大学技术与创新学院）；Information Technology, Åbo Akademi University（阿博阿卡迪米大学信息技术系）；Faculty of Information Technology and Communication Sciences, Tampere University（塔尔库大学信息科技与通信科学学院）；Finnish Geospatial Research Institute（芬兰地理空间研究 institute）
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…
High-precision vehicle positioning is key to the implementation of modern driving systems in urban environments. Global Navigation Satellite System (GNSS) carrier phase measurements can provide millimeter- to centimeter-level positioning, provided that the integer ambiguities are correctly resolved. Abundant code measurements are often used to facilitate integer ambiguity resolution (IAR), however, they suffer from signal blockage and multipath in urban canyons. In this contribution, a lidar-aided instantaneous ambiguity resolution method is proposed. Lidar measurements, in the form of 3D keyp…
Stanford University（斯坦福大学）；University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Indian Institute of Technology Bombay（印度理工学院孟买分校）；Indian Institute of Technology Kanpur（印度理工学院坎普尔分校）
Deep Neural Networks (DNNs) are a promising tool for Global Navigation Satellite System (GNSS) positioning in the presence of multipath and non-line-of-sight errors, owing to their ability to model complex errors using data. However, developing a DNN for GNSS positioning presents various challenges, such as 1) poor numerical conditioning caused by large variations in measurements and position values across the globe, 2) varying number and order within the set of measurements due to changing satellite visibility, and 3) overfitting to available data. In this work, we address the aforementioned …
Robust Statistics vs. Machine Learning vs. Bayesian Inference: Insights into Handling Faulty GNSS Measurements in Field Robotics
Institute of Automatic Control, RWTH Aachen University（鲁尔大学自动控制研究所）；Learning Systems and Robotics Lab, Technical University of Munich（慕尼黑技术大学学习系统与机器人实验室）；Munich Institute of Robotics and Machine Intelligence (MIRMI)（慕尼黑机器人与机器智能研究所）
Real-time tightly coupled GNSS and IMU integration via Factor Graph Optimization
University Politehnica of Bucharest（巴特亚大学）；Three Tensors S.R.L.（Three Tensors公司）；University of Bucharest（布加勒斯特大学）；Navigation Systems Definition Section (TEC-SEN), ESA（欧洲航天局导航系统定义部门）；Romanian InSpace Engineering S.R.L. (RISE)（罗马尼亚InSpace工程公司）
Robust Position Estimation by Rao-Blackwellized Particle Filter without Integer Ambiguity Resolution in Urban Environments
Department of Mechatronics Engineering Graduate School of Meijo University（机械电子工程系 Meijo大学研究生院）；Department of Mechatronics Engineering Meijo University（机械电子工程系 Meijo大学）；Future Robotics Technology Center Chiba Institute of Technology（未来机器人技术中心 Chiba工业大学）
