Multipath Estimation in Urban Environments from Joint GNSS Receivers and LiDAR Sensors

In this paper, multipath error on Global Navigation Satellite System (GNSS) signals in urban environments is characterized with the help of Light Detection and Ranging (LiDAR) measurements. For this purpose, LiDAR equipment and Global Positioning System (GPS) receiver implementing a multipath estimating architecture were used to collect data in an urban environment. This paper demonstrates how GPS and LiDAR measurements can be jointly used to model the environment and obtain robust receivers. Multipath amplitude and delay are estimated by means of LiDAR feature extraction and multipath mitigation architecture. The results show the feasibility of integrating the information provided by LiDAR sensors and GNSS receivers for multipath mitigatio

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

Analysing the effects of sensor fusion, maps and trust models on autonomous vehicle satellite navigation positioning

This thesis analyzes the effects of maps, sensor fusion and trust models on autonomous vehicle satellite positioning. The aim is to analyze the localization improvements that commonly used sensors, technologies and techniques provide to autonomous vehicle positioning. This thesis includes both survey of localization techniques used by other research and their localization accuracy results as well as experimentation where the effects of different technologies and techniques on lateral position accuracy are reviewed. The requirements for safe autonomous driving are strict and while the performance of the average global navigation satellite system (GNSS) receiver alone may not prove to be adequate enough for accurate positioning, it may still provide valuable position data to an autonomous vehicle. For the vehicle, this position data may provide valuable information about the absolute position on the globe, it may improve localization accuracy through sensor fusion and it may act as an independent data source for sensor trust evaluation. Through empirical experimentation, the effects of sensor fusion and trust functions with an inertial measurement unit (IMU) on GNSS lateral position accuracy are measured and analyzed. The experimentation includes the measurements from both consumer-grade devices mounted on a traditional automobile and high-end devices of a truck that is capable of autonomous driving in a monitored environment. The maps and LIDAR measurements used in the experiments are prone to errors and are taken into account in the analysis of the data

Towards secure & robust PNT for automated systems

This dissertation makes four contributions in support of secure and robust position, navigation, and timing (PNT) for automated systems. The first two relate to PNT security while the latter two address robust positioning for automated ground vehicles. The first contribution is a fundamental theory for provably-secure clock synchronization between two agents in a distributed automated system. All one-way synchronization protocols, such as those based on the Global Positioning System (GPS) and other Global Navigation Satellite Systems (GNSS), are shown to be vulnerable to man-in-the-middle delay attacks. This contribution is the first to identify the necessary and sufficient conditions for provably secure clock synchronization. The second contribution, also related to PNT security, is a three-year study of the world-wide GPS interference landscape based on data from a dual-frequency GNSS receiver operating continuously on the International Space Station (ISS). This work is the first publicly-reported space-based survey of GNSS interference, and unveils previously-unreported GNSS interference activity. The third contribution is a novel ground vehicle positioning technique that is robust to GNSS signal blockage, poor lighting conditions, and adverse weather events such as heavy rain and dense fog. The technique relies on sensors that are commonly available on automated vehicles and are insensitive to lighting and inclement weather: automotive radar, low-cost inertial measurement units (IMUs), and GNSS. Remarkably, it is shown that, given a prior radar map, the proposed technique operating on data from off-the-shelf all-weather automotive sensors can maintain sub-50-cm horizontal position accuracy during 60 min of GNSS-denied driving in downtown Austin, TX. This dissertation’s final contribution is an analysis and demonstration of the feasibility of crowd-sourced digital mapping for automated vehicles. Localization techniques, such as the one described in the previous contribution, rely on such digital maps for accuracy and robustness. A key enabler for large-scale up-to-date maps is enlisting the help of the very consumer vehicles that need the map to build and update it. A method for fusing multi-session vision data into a unified digital map is developed. The asymptotic limit of such a map’s globally-referenced position accuracy is explored for the case in which the mapping agents rely on low-cost GNSS receivers performing standard code-phase-based navigation. Experimental validation along a semi-urban route shows that low-cost consumer vehicles incrementally tighten the accuracy of the jointly-optimized digital map over time enough to support sub-lane-level positioning in a global frame of reference.Electrical and Computer Engineerin

Vertiport Navigation Requirements and Multisensor Architecture Considerations for Urban Air Mobility

Communication, Navigation and Surveillance (CNS) technologies are key enablers for future safe operation of drones in urban environments. However, the design of navigation technologies for these new applications is more challenging compared to e.g., civil aviation. On the one hand, the use cases and operations in urban environments are expected to have stringent requirements in terms of accuracy, integrity, continuity and availability. On the other hand, airborne sensors may not be based on high-quality equipment as in civil aviation and solutions need to rely on tighter multisensor solutions, whose safety is difficult to assess. In this work, we first provide some initial navigation requirements related to precision approach operations based on recently proposed vertiport designs. Then, we provide an overview of a possible multisensor navigation architecture solution able to support these types of operations and we comment on the challenges of each of the subsystems. Finally, initial proof of concept for some navigation sensor subsystems is presented based on flight trials performed during the German Aerospace Center (DLR) project HorizonUAM

Recent Advancement on the Use of Global Navigation Satellite System-Based Positioning for Intelligent Transport Systems

Non peer reviewe

Fast and Precise Neural Network-Based Environment Detection utilizing UWB CSI for Seamless Localization Applications

Seamless localization, navigation, and tracking applications can be realized utilizing different sensors and cameras, radio frequency signals such as WiFi, ultra-wideband, and global navigation satellite system, each of which is better suited for different types of environments. As such, awareness of the environment is crucial for the system to efficiently utilize the most relevant resources in each scenario and enable seamless transition between different environments. For example, when vehicles are moving from an open area such as open highway to crowded urban streets, or the opposite, they experience a considerable environment transition, which triggers opportunities for wide-range environment-specific device and algorithm optimization. In this paper, a novel infrastructure-free method utilizing channel state information of ultra-wideband signals and a convolutional neural network is proposed. This method enables a fast detection of the environment type, including crowded urban and open outdoor, reaching a detection latency of only three milliseconds. The experimental data is collected in the real environments of the city of Ghent, Belgium. The test data set, used for numerical performance evaluations, is collected from areas different from those used in the training set. The results show that the proposed method provides an average environment detection accuracy of 90% in the considered test setup.Peer reviewe

Graphical SLAM for urban UAV navigation

In recent years, there has been rising interest in commercial applications of Unmanned Air Vehicles (UAVs). Examples of their usage include aerial photography, infrastructure inspection and emergency first response. For widespread commercial adoption of these applications to occur, UAV navigation must be made safe and reliable. In open-sky environments, Global Positioning System (GPS) receivers are most commonly used to provide accurate and globally referenced positioning for UAVs. However, many applications, such as consumer product delivery, require UAVs to operate in densely populated urban environments. In these environments, buildings and structures reflect and block GPS signals, leading to multipath and low satellite visibility. These factors create GPS-challenged environments that result in large errors in UAV positioning or make GPS unavailable. To improve urban GPS, one approach uses environment modeling such as 3D city models to mitigate the effects of multipath and NLOS errors. Others pair GPS with odometry measurements from relative positioning sensors, such as Light Detection and Ranging (LiDAR) sensors. LiDAR-based odometry provides an accurate relative navigation solution in GPS-challenged environments, but requires distinguishable features in the surrounding environment and is susceptible to drift and biases. As a result, there is a need for sensor fusion techniques that can provide reliable and robust positioning in urban environments. In this thesis, we apply a Simultaneous Localization and Mapping (SLAM) approach to fuse GPS pseudorange measurements with LiDAR point clouds and 3D building footprint data of the existing region, for UAV trajectory estimation and environment mapping. Our approach consists of three main aspects: graphical modeling, map-based processing, and inference. First, we use a probabilistic graph, specifically a directed acyclic graph, to model the trajectory of a UAV. Nodes in the graph represent states of the UAV or GPS satellites; while edges represent relations between states created by sensor measurements. We then use the graph to structure our environment maps. We represent our environment in two mapping formats: a point cloud map and an urban building map. The point cloud map is formulated by anchoring each collected LiDAR point cloud with their respective state. The result is a large point cloud collected throughout the trajectory of the UAV. The urban building map is a geometric representation of the large scale structures in the environment. It is first initialized with available sources of 3D building model data for the navigating region. In this work, we use Champaign building footprint data from the State of Illinois data portal. We then run plane fitting algorithms on the collected point clouds at each state to update the urban building map. As the UAV navigates, the graph is populated by additional nodes and corresponding LiDAR measurements, allowing for SLAM of the environment. Next, we apply the formulated maps in two ways: mitigation of errors resultant from reflected GPS signals; and map matching with existing or previously collected maps of the region. The urban building map is first initialized with city building footprint data. We then draw line-of-sight vectors to from the UAV to each satellite and identify NLOS satellites from intersections with the urban building map. Next, we use density of surrounding buildings to identify potential satellite measurements affected by multipath. After identifying multipath-affected GPS measurements, we propose a multipath model via covariance adjustment to deweigh their effects on the UAV state estimate. We then append our graph with additional map matching edges. Based on the probabilistic distribution of a state, we generate and propagate particles representing potential states of the UAV. Then using the urban map, we compare the expected buildings observed by each particle with the buildings observed from the LiDAR measurements. We find the most likely particle and use it as a constraint measurement in the graph. We then compare the point cloud collected at each time step with the point cloud map to perform loop closure and create constraint measurements to the initial position of the UAV. Afterwards, we take a probabilistic approach towards trajectory estimation in the graphical inference step. Using the edges directed at the UAV nodes in the graph, we formulate a joint probability that represents the likelihood of the state estimate given the collected measurements. We then perform inference on the graph and formulate a Maximum A Posteriori (MAP) estimate of the UAV trajectory. Since the collected LiDAR point clouds are anchored at the corresponding state estimates, we simultaneously optimize for the maps generated by the system. Finally, we experimentally validate our algorithm by presenting the results of a series of UAV flight tests in both GPS-challenged and GPS-friendly environments near and on the University of Illinois at Urbana-Champaign campus. We show that our probabilistic graphical sensor fusion approach provides an accurate and available navigation solution that allows a UAV to navigate an urban environment under the presence of GPS signal reflections and occlusions

Improving the robustness of GPS direct position estimation

Global Positioning System (GPS) receivers are increasingly used for positioning in urban environments and precise timing in critical infrastructures. These scenarios are challenging for GPS receivers because building reflection and obstruction contribute to GPS signal degradation in urban environments, while potential jamming and spoofing attacks disrupt GPS time synchronization in critical infrastructures. We propose using Direct Position Estimation (DPE), augmented with additional navigation information, to enable robust GPS receiver operation in challenging scenarios. Unlike conventional methods, such as scalar and vector tracking, DPE performs Maximum Likelihood Estimation (MLE) of the navigation solution on the raw GPS signal. DPE initializes multiple navigation candidates and searches for the candidate that maximizes the cross-correlation between the expected GPS signal reception and the received GPS signal. The direct search and inherent joint optimization across multiple satellite signals make DPE more robust than scalar and vector tracking. In addition, since the parameter of interest is the navigation solution, DPE provides a natural framework for directly incorporating additional navigation information. The contribution of this thesis is to design and experimentally validate algorithms for deeply integrating additional navigation information into DPE. To improve the robustness of DPE in multipath caused by building reflection, we propose transforming non-line-of-sight (NLOS) GPS signals from being unwanted interferences to useful navigation signals. We include NLOS GPS signals into the expected GPS signal reception as additional line-of-sight (LOS) GPS signals to virtual satellites at mirror-image positions. The satellite mirror-image positions are calculated using information of building reflection surfaces, estimated from available three-dimensional (3D) maps. We conducted experiments in front of the 50~m by 40~m wind tunnel located at NASA's Ames Research Center in Mountain View, California, utilizing the surface of the wind tunnel as a reflector of GPS signals. We demonstrated through our experiment, improved robustness in terms of horizontal positioning accuracy, due to the constructive integration of NLOS GPS signals. In urban environments where GPS sensing is hindered by building obstruction, we propose addressing buildings as additional navigation features instead of undesirable obstacles. We deeply integrate DPE with image map-matching of images captured by an onboard camera against geo-referenced images. The navigation solutions directly estimated from both DPE and image map-matching are fused and used in close-loop GPS signal and camera image tracking. We conducted experiments with joint collections of GPS signals and camera images on our university campus in Urbana, Illinois. We demonstrated, through our experiment, improved robustness in terms of positioning availability, due to the additional vision information. In addition to positioning, GPS receivers are used for time synchronization in critical infrastructures, where they are vulnerable to malicious attacks. For robust GPS time estimation, we propose using the known, static GPS receiver location as prior information. Estimation of the 3D position, clock bias, 3D velocity and clock drift parameters is reduced to estimation of only the clock bias and clock drift parameters. We conducted experiments on the rooftop of our laboratory in Urbana, Illinois, using the collected signals in simulated jamming and spoofing attacks. We demonstrated improved robustness in terms of anti-jamming and anti-spoofing, due to the information redundancy gained from parameter reduction