Navigation In Urban Environments Research Articles

GNSS/IMU/LO integration with a new LO error model and lateral constraint for navigation in urban areas

The quest for reliable vehicle navigation in urban environments has led the integration of Light Detection and Ranging (LiDAR) Odometry (LO) with Global Navigation Satellite Systems (GNSS) and Inertial Measurement Units (IMU). However, the performance of the integrated system is limited by a lack of accurate LO error modeling. In this paper, we propose a weighted GNSS/IMU/LO integration-based navigation system with a novel LO error model. The Squared Exponential Gaussian Progress Regression (SE-GPR) based LO error model is developed by considering the vehicle velocity and number of point cloud features. Based on error prediction for GNSS positioning and LO, a weighting strategy is designed for integration in an Extended Kalman Filter (EKF). Furthermore, error accumulation of the navigation state, especially in GNSS-challenging scenarios, is restrained by the LiDAR-Aided Lateral Constraint (LALC) and Non-Holonomic Constraint (NHC). An experiment was conducted in a deep urban area to test the proposed algorithm. The results show that the proposed algorithm delivers horizontal and three-dimensional (3D) positioning Root Mean Square Errors (RMSEs) of 3.669 m and 5.216 m, respectively. The corresponding accuracy improvements are 35.9% and 50.0% compared to the basic EKF based GNSS/IMU/LO integration.

A Robust Evolutionary Particle Filter Technique for Integrated Navigation in Urban Environments via GNSS and 5G Signals

A Robust Evolutionary Particle Filter Technique for Integrated Navigation in Urban Environments via GNSS and 5G Signals

A Novel Machine Learning-Based ANFIS Calibrated RISS/GNSS Integration for Improved Navigation in Urban Environments.

Autonomous vehicles (AVs) require accurate navigation, but the reliability of Global Navigation Satellite Systems (GNSS) can be degraded by signal blockage and multipath interference in urban areas. Therefore, a navigation system that integrates a calibrated Reduced Inertial Sensors System (RISS) with GNSS is proposed. The system employs a machine-learning-based Adaptive Neuro-Fuzzy Inference System (ANFIS) as a novel calibration technique to improve the accuracy and reliability of the RISS. The ANFIS-based RISS/GNSS integration provides a more precise navigation solution in such environments. The effectiveness of the proposed integration scheme was validated by conducting tests using real road trajectory and simulated GNSS outages ranging from 50 to 150 s. The results demonstrate a significant improvement in 2D position Root Mean Square Error (RMSE) of 43.8% and 28% compared to the traditional RISS/GNSS and the frequency modulated continuous wave (FMCW) Radar (Rad)/RISS/GNSS integrated navigation systems, respectively. Moreover, an improvement of 47.5% and 23.4% in 2D position maximum errors is achieved compared to the RISS/GNSS and the Rad/RISS/GNSS integrated navigation systems, respectively. These results reveal significant improvements in positioning accuracy, which is essential for safe and efficient navigation. The long-term stability of the proposed system makes it suitable for various navigation applications, particularly those requiring continuous and precise positioning information. The ANFIS-based approach used in the proposed system is extendable to other low-end IMUs, making it an attractive option for a wide range of applications.

Improving Pedestrian Navigation in Urban Environment Using Augmented Reality and Landmark Recognition

Improving Pedestrian Navigation in Urban Environment Using Augmented Reality and Landmark Recognition

Improving PPP-RTK-based vehicle navigation in urban environments via multilayer perceptron-based NLOS signal detection

Improving PPP-RTK-based vehicle navigation in urban environments via multilayer perceptron-based NLOS signal detection

FGO-GIL: Factor Graph Optimization-Based GNSS RTK/INS/LiDAR Tightly Coupled Integration for Precise and Continuous Navigation

As a relative positioning technique, LiDAR-Inertial Odometry (LIO) is known to suffer from drifting and can only provide local coordinates. To compensate for these shortages of LIO, an effective way is to integrate global navigation satellite system (GNSS) with LIO. In this contribution, we proposed a tightly coupled GNSS RTK/INS/LiDAR system under the factor graph optimization framework, termed as FGO-GIL, to achieve high-precision and continuous navigation in urban environments. This integration system fuses raw GNSS measurements (i.e., pseudorange and carrier phase measurements) with IMU and LiDAR information at the observation level atop a factor graph. Moreover, a keyframe-based nonlinear optimization scheme is designed to efficiently utilize measurements from the mixed heterogeneous sensors. Specifically, non-keyframes are united with IMU pre-integration for inter-frame optimization, which can provide accurate and high-frequency state predictions for the scan-matching of keyframes. Sparse keyframes are used to construct LiDAR factors through matching with the submap for sliding window optimization. To evaluate the effectiveness of our approach, real-world experiments are conducted in both campus and urban environments. The results demonstrate that our system can achieve continuous decimeter-level positioning accuracy in these complex environments, outperforming other state-of-the-art frameworks.

Radar/INS Integration and Map Matching for Land Vehicle Navigation in Urban Environments.

Autonomous navigation requires multi-sensor fusion to achieve a high level of accuracy in different environments. Global navigation satellite system (GNSS) receivers are the main components in most navigation systems. However, GNSS signals are subject to blockage and multipath effects in challenging areas, e.g., tunnels, underground parking, and downtown or urban areas. Therefore, different sensors, such as inertial navigation systems (INSs) and radar, can be used to compensate for GNSS signal deterioration and to meet continuity requirements. In this paper, a novel algorithm was applied to improve land vehicle navigation in GNSS-challenging environments through radar/INS integration and map matching. Four radar units were utilized in this work. Two units were used to estimate the vehicle's forward velocity, and the four units were used together to estimate the vehicle's position. The integrated solution was estimated in two steps. First, the radar solution was fused with an INS through an extended Kalman filter (EKF). Second, map matching was used to correct the radar/INS integrated position using OpenStreetMap (OSM). The developed algorithm was evaluated using real data collected in Calgary's urban area and downtown Toronto. The results show the efficiency of the proposed method, which had a horizontal position RMS error percentage of less than 1% of the distance traveled for three minutes of a simulated GNSS outage.

A factor set-based GNSS fault detection and exclusion for vehicle navigation in urban environments

With the rapid development of safety–critical applications of Intelligent Transportation Systems, Global Navigation Satellite System (GNSS) fault detection and exclusion (FDE) methods have made navigation systems increasingly reliable. However, in multi-fault cases of urban environments, FDE methods generally demand massive calculations and have a high risk of missed detection and false alarm. To deal with this issue, we proposed a factor set-based FDE algorithm for integrating GNSS and Inertial Measurement Units (IMU). The FDE is first performed efficiently via consistency checking over far fewer subsets of the pseudorange. Afterward, the FDE results are validated by missed-detection and false-alarm checks. The missed-detection check factor is designed by predicting the maximum horizontal GNSS positioning error, while the false-alarm check factor is designed with the aid of IMU mechanization. Following FDE, a loosely coupled GNSS/IMU integration is carried out to output the final estimation of the vehicle's position, velocity and attitude. The proposed algorithm improves both horizontal and 3D positioning accuracy by more than 50% in the field test, compared to the traditional GNSS/IMU loosely coupled scheme. Additionally, with the proposed algorithm, the accuracy improvements of the velocity and the heading are over 20% and 50%, respectively.

Tightly Coupled Integration of GNSS, INS, and LiDAR for Vehicle Navigation in Urban Environments

The emerging Internet of Things (IoT) applications, such as driverless cars, have a growing demand for high-precision positioning and navigation. Nowadays, the global navigation satellite system (GNSS) is recognized as an important approach for worldwide positioning services. However, its application is limited in urban areas due to severe signal attenuation, reflections, and blockages. Inertial navigation system (INS) can provide high-precision navigation outputs within a short period, but its accuracy suffers from error accumulation, especially when equipped with the low-cost microelectromechanical system (MEMS) inertial measurement units (IMUs). In addition, light detection and ranging (LiDAR) is becoming more common as an option in vehicles, which can detect rich geometric information in the environment for ego-motion estimation. Aiming at taking advantage of the complementary characteristics of these onboard technologies to navigate in urban environments, a tightly coupled multi-GNSS precise point positioning (PPP)/INS/LiDAR integrated system is proposed. We also develop an LiDAR sliding-window plane-feature tracking method to further improve navigation accuracy and computational efficiency. The performance of the proposed integrated system was evaluated in vehicular experiments with different GNSS observation conditions. Results indicate that our proposed GNSS/INS/LiDAR integration can maintain submeter level horizontal positioning accuracy in GNSS-challenging environments, with improvements of (73.3%, 59.7%, and 64.2%) compared to traditional GNSS/INS integration. Moreover, the plane-feature tracking method is proved to outperform traditional point-to-line and point-to-plane scan matching in terms of accuracy and efficiency.

Continuous and Precise Positioning in Urban Environments by Tightly Coupled Integration of GNSS, INS and Vision

Accurate, continuous and seamless state estimation is the fundamental module for intelligent navigation applications, such as self-driving cars and autonomous robots. However, it is often difficult for a standalone sensor to fulfill the demanding requirements of precise navigation in complex scenarios. To fill this gap, this letter proposes to exploit the complementariness of the global navigation satellite system (GNSS), inertial measurement unit (IMU) and vision via a tightly coupled integration method, aiming to achieve continuous and accurate navigation in urban environments. Specifically, the raw GNSS carrier phase and pseudorange measurements, IMU data, and visual features are directly fused at the observation level through a centralized Extended Kalman Filter (EKF) to make full use of the multi-sensor information and reject potential outlier measurements. Furthermore, the widely used high-precision GNSS models including precise point positioning (PPP) and real-time kinematic (RTK) are unified in the proposed integrated system to increase usability and flexibility. We validate the performance of the proposed method on several challenging datasets collected in urban canyons and compare against the loosely coupled and state-of-the-art methods.

Multi-GNSS PPP/INS/Vision/LiDAR tightly integrated system for precise navigation in urban environments

Nowadays, high-precision navigation is a fundamental prerequisite for autonomous driving technology. However, it is often difficult for a stand-alone sensor to meet the needs of high-precision navigation in complex scenarios. To address this problem, we propose a tightly coupled precise point positioning (PPP)/inertial navigation system (INS)/Vision/LiDAR integration method to achieve high-precision, continuous and reliable navigation in urban environments. The multi-GNSS carrier phase and pseudorange measurements, low-cost microelectromechanical system (MEMS) inertial measurement units (IMU) records, sparse visual landmarks and extracted LiDAR edge/planar features are directly fused at the observation level through a centralized Extended Kalman Filter (EKF). The vehicle experiments in different GNSS availability conditions were conducted to evaluate the proposed method. Results indicate that our proposed PPP/INS/Vision/LiDAR integration can maintain sub-meter level positioning in both GNSS half-open-sky and difficult environments, with improvements of (50.7%, 58.6%, 54.3%) and (46.2%, 55.0%, 58.8%) relative to PPP/INS/Vision and PPP/INS/LiDAR, respectively. Moreover, both visual and LiDAR information can significantly improve the velocity and attitude estimation performance, especially for the heading.

Centimeter-accurate vehicle navigation in urban environments with a tightly integrated PPP-RTK/MEMS/vision system

Centimeter-accurate vehicle navigation in urban environments with a tightly integrated PPP-RTK/MEMS/vision system

Kalman Filter-Based Integrity Monitoring for GNSS and 5G Signals of Opportunity Integrated Navigation

A Kalman filter-based receiver autonomous integrity monitoring algorithm (RAIM) is proposed to exploit sequential measurements from global navigation satellite systems (GNSS) and cellular 5G signals of opportunity (SOPs), to ensure safe vehicular navigation in urban environments. To deal with frequent threats caused by multipath and non-line-of-sight conditions, an innovation-based outlier rejection method is introduced. Next, a fault detection technique based on solution separation test is developed, and the quantification of protection levels is derived. Experimental results of a ground vehicle traveling in an urban environment, while making pseudorange measurements to GPS satellites and cellular 5G towers, are presented to demonstrate the efficacy of the proposed method. Incorporating 5G signals from only 2 towers is shown to reduce the horizontal protection level (HPL) by 0.22 m compared to using only GPS. Moreover, the proposed method is shown to reduce the HPL and vertical protection level (VPL) by 84.42% and 69.63%, respectively, over the snapshot advanced RAIM (ARAIM).

GIL: a tightly coupled GNSS PPP/INS/LiDAR method for precise vehicle navigation

Accurate positioning and navigation play a vital role in vehicle-related applications, such as autonomous driving and precision agriculture. With the rapid development of Global Navigation Satellite Systems (GNSS), Precise Point Positioning (PPP) technique, as a global positioning solution, has been widely applied due to its convenient operation. Nevertheless, the performance of PPP is severely affected by signal interference, especially in GNSS-challenged environments. Inertial Navigation System (INS) aided GNSS can significantly improve the continuity and accuracy of navigation in harsh environments, but suffers from degradation during GNSS outages. LiDAR (Laser Imaging, Detection, and Ranging)-Inertial Odometry (LIO), which has performed well in local navigation, can restrain the divergence of Inertial Measurement Units (IMU). However, in long-range navigation, error accumulation is inevitable if no external aids are applied. To improve vehicle navigation performance, we proposed a tightly coupled GNSS PPP/INS/LiDAR (GIL) integration method, which tightly integrates the raw measurements from multi-GNSS PPP, Micro-Electro-Mechanical System (MEMS)-IMU, and LiDAR to achieve high-accuracy and reliable navigation in urban environments. Several experiments were conducted to evaluate this method. The results indicate that in comparison with the multi-GNSS PPP/INS tightly coupled solution the positioning Root-Mean-Square Errors (RMSEs) of the proposed GIL method have the improvements of 63.0%, 51.3%, and 62.2% in east, north, and vertical components, respectively. The GIL method can achieve decimeter-level positioning accuracy in GNSS partly-blocked environment (i.e., the environment with GNSS signals partly-blocked) and meter-level positioning accuracy in GNSS difficult environment (i.e., the environment with GNSS hardly used). Besides, the accuracy of velocity and attitude estimation can also be enhanced with the GIL method.

A Multi-Objective Coverage Path Planning Algorithm for UAVs to Cover Spatially Distributed Regions in Urban Environments

This paper presents a multi-objective coverage flight path planning algorithm that finds minimum length, collision-free, and flyable paths for unmanned aerial vehicles (UAV) in three-dimensional (3D) urban environments inhabiting multiple obstacles for covering spatially distributed regions. In many practical applications, UAVs are often required to fully cover multiple spatially distributed regions located in the 3D urban environments while avoiding obstacles. This problem is relatively complex since it requires the optimization of both inter (e.g., traveling from one region/city to another) and intra-regional (e.g., within a region/city) paths. To solve this complex problem, we find the traversal order of each area of interest (AOI) in the form of a coarse tour (i.e., graph) with the help of an ant colony optimization (ACO) algorithm by formulating it as a traveling salesman problem (TSP) from the center of each AOI, which is subsequently optimized. The intra-regional path finding problem is solved with the integration of fitting sensors’ footprints sweeps (SFS) and sparse waypoint graphs (SWG) in the AOI. To find a path that covers all accessible points of an AOI, we fit fewer, longest, and smooth SFSs in such a way that most parts of an AOI can be covered with fewer sweeps. Furthermore, the low-cost traversal order of each SFS is computed, and SWG is constructed by connecting the SFSs while respecting the global and local constraints. It finds a global solution (i.e., inter + intra-regional path) without sacrificing the guarantees on computing time, number of turning maneuvers, perfect coverage, path overlapping, and path length. The results obtained from various representative scenarios show that proposed algorithm is able to compute low-cost coverage paths for UAV navigation in urban environments.

Road Curb Detection: A Historical Survey.

Curbs are used as physical markers to delimit roads and to redirect traffic into multiple directions (e.g., islands and roundabouts). Detection of road curbs is a fundamental task for autonomous vehicle navigation in urban environments. Since almost two decades, solutions that use various types of sensors, including vision, Light Detection and Ranging (LiDAR) sensors, among others, have emerged to address the curb detection problem. This survey elaborates on the advances of road curb detection problems, a research field that has grown over the last two decades and continues to be the ground for new theoretical and applied developments. We identify the tasks involved in the road curb detection methods and their applications on autonomous vehicle navigation and advanced driver assistance system (ADAS). Finally, we present an analysis on the similarities and differences of the wide variety of contributions.

A new IMU-aided multiple GNSS fault detection and exclusion algorithm for integrated navigation in urban environments

A new IMU-aided multiple GNSS fault detection and exclusion algorithm for integrated navigation in urban environments

Aha! I know where I am: the contribution of visuospatial cues to reorientation in urban environments

ABSTRACT Reorientation depends greatly on the perceived geometric information, which constantly changes during navigation in urban environments. Environmental novelty, as a driver of exploratory behavior, is likely to engender this spatial Aha! moment. The paper investigates the contribution of two qualitatively different types of novelty, corresponding to distinct visuospatial cues: (a) situations that cause surprise, e.g., a sudden change in spaciousness; versus (b) situations that engender mystery, e.g., a change in the complexity of visuospatial information and the promise of gaining new information. Visibility graph analysis is used to quantify and examine these hypotheses in relation to participants’ exploratory behavior and brain dynamics (EEG) during virtual navigation. The findings suggest that reorientation is a spatial boundary effect, associated primarily with a change in visuospatial complexity.

Multipath modeling and mitigation by using sparse estimation in global navigation satellite system-challenged urban vehicular environments

Multipath interference has been one of the most difficult problems when using global navigation satellite system-based vehicular navigation in urban environments. In this article, we develop a multipath mitigation algorithm exploiting the sparse estimation theory that improves the absolute positioning accuracy in urban environments. The navigation observation model is established by considering the multipath bias as additive positioning errors, and the assumption for the proposed method is that global navigation satellite system signals contaminated due to multipath are the minority among the received signals, which makes the unknown bias vector sparse. We investigated an improved elastic net method to estimate the sparse multipath bias vector, and the global navigation satellite system measurements can be corrected by subtracting the estimated multipath error. The positioning performance of the proposed method is verified by analytical and experimental results.

Platooning of Car-Like Vehicles in Urban Environments: An Observer-Based Approach Considering Actuator Dynamics and Time Delays

In this paper, a distributed observer-based approach is proposed to control the longitudinal motion of car-like vehicle platoon moving in an urban environment. To the best of our knowledge, this is the first work presenting an observer-based platoon controller that combines the advantages of high traffic capacity and a minimum number of communication links. To achieve a high traffic flow, a constant-spacing policy is used. However, for that policy, to make platoon string stable, the leader information must be broadcast to all the vehicles. Therefore, we propose a control law in which the predecessor position information is acquired by a sensor-based link while a communication-based link is used to obtain the leader information. Then, an observer is designed and integrated into the control law such that the velocity information of the predecessor can be estimated without the need to communicate with the preceding vehicle. For navigation in urban environments, we present a third order platoon model represented in the curvilinear coordinates. Conditions for asymptotic stability and string stability are given considering the vehicle actuator dynamics and the induced network/sensor time delay. Finally, we provide both simulation and real-time results to validate our approach feasibility and to corroborate our theoretical findings.