Navigation In Urban Areas 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.

Measurement Quality Control Aided Multisensor System for Improved Vehicle Navigation in Urban Areas

Measurement Quality Control Aided Multisensor System for Improved Vehicle Navigation in Urban Areas

Walking Gaits Aided Mobile GNSS for Pedestrian Navigation in Urban Areas

Walking Gaits Aided Mobile GNSS for Pedestrian Navigation in Urban Areas

Multi-objective reinforcement learning for autonomous drone navigation in urban areas with wind zones

Drones can be used for tasks such as data collection and logistics in civil engineering. Current research on autonomous drones mainly focuses on planning a safe path and avoiding obstacles in a static environment. However, navigating a drone in complex environments like urban areas involves many dynamic constraints, such as building layout, winds, and signal coverage, which are interdependent. The wind factor is the most important among these environmental factors, which may cause a drone to lose control or even crash. This paper presents a multi-objective navigation reinforcement learning algorithm (MONRL) for the drone to navigate and avoid obstacles in an unknown environment when dynamic wind zones present, with only imagery data about the building layouts. Based on a deep reinforcement learning and memory architecture, the drone develops policies to prioritize navigation decisions, optimizing the path while minimizing negative impact of winds with only sparse sensor data, in our case, camera inputs. By leveraging the advantages of the proposed method in estimating the environmental factors from previous trials, no aerodynamic force sensors are needed for the drone to develop effective strategies to navigate to target while counteracting to milder winds and dodging aways from stronger winds. The proposed method was tested in a virtual environment, and a real model of New York City. The method is expected to contribute to new automation algorithms for urban aerial logistics and future automated civil infrastructure inspection.

An Adaptive Weighting Strategy for Multisensor Integrated Navigation in Urban Areas

Integration of global navigation satellite systems (GNSS) with other sensors, such as inertial measurement units (IMU) and visual sensors, has been widely used to improve the positioning accuracy and availability of the vehicles for the Internet of Things (IoT) applications in smart cities. The traditional Extended Kalman Filter (EKF)-based fusion scheme, with the assumption of fixed measurements of different sensors and inaccurate GNSS quality assessment, is vulnerable to Non-line-of-Sight (NLOS) and multipath contaminated GNSS, as well as low-quality vision measurement. In order to tackle this issue, we have proposed an adaptive weighting strategy for GNSS/IMU/Vision integration. On the basis of dual-check GNSS assessment, we adjust the weights of the vision and GNSS measurements adaptively based on the chi-square test statistic. The field tests have demonstrated that the proposed algorithm achieves horizontal positioning root mean square errors (RMSEs) of 11.92 m and 3.61 m in deep and mild urban environments. The accuracy has improvements of 78.57% and 43.9% over traditional EKF-based GNSS/IMU fusion, and 21.53% and 23.49% over compared EKF-based GNSS/IMU/Vision fusion, respectively.

Evaluation and Implementation of Traffic Clearance for Emergency Applications

In this project, the ESP8266 microcontroller and LEDs are utilized to create a system that effectively controls traffic flow during emergency situations. Two modules, an emergency vehicle module, and a traffic module, communicate with a server. The server receives signals from the emergency vehicle and triggers the corresponding traffic lights to turn green and yellow in the direction of the vehicle. Simultaneously, the traffic lights in other directions turn red and yellow, providing a clear path for the emergency vehicle. To enhance safety and awareness, a mobile application is developed. Users between the emergency vehicle and the traffic signal can install the app, which alerts them of the approaching vehicle. Real-time updates, sound alerts, and vibrations are provided to ensure the public is aware and can take appropriate actions. An additional app is designed for the emergency vehicle driver, allowing manual control over traffic signals when they are positioned too closely together. This ensures the vehicle can navigate intersections without hindrance. By combining the server, mobile apps, and ESP8266 microcontroller, this project offers a comprehensive solution to improve emergency vehicle navigation in urban areas. It optimizes traffic flow, enhances public safety, and provides additional control for emergency vehicle drivers. The goal is to provide an efficient and accessible solution that can be implemented in various urban settings.

Integration of Multi-GNSS PPP-RTK/INS/Vision with a Cascading Kalman Filter for Vehicle Navigation in Urban Areas

Precise point positioning (PPP) has received much attention in recent years for its low cost, high accuracy, and global coverage. Nowadays, PPP with ambiguity resolution and atmospheric augmentation is widely regarded as PPP-RTK (real-time kinematic), which weakens the influence of the long convergence time in PPP and regional service coverage in RTK. However, PPP-RTK cannot work well in urban areas due to limitations of non-line-of-sight (NLOS) conditions. Inertial navigation systems (INS) and vision can realize continuous navigation but suffer from error accumulation. Accordingly, the integration model of multi-GNSS (global navigation satellite system) and PPP-RTK/INS/vision with a cascading Kalman filter and dynamic object removal model was proposed to improve the performance of vehicle navigation in urban areas. Two vehicular tests denoted T01 and T02 were conducted in urban areas to evaluate the navigation performance of the proposed model. T01 was conducted in a relatively open-sky environment and T02 was collected in a GNSS-challenged environment with many obstacles blocking the GNSS signals. The positioning results show that the dynamic object removal model can work well in T02. The results indicate that multi-GNSS PPP-RTK/INS/vision with a cascading Kalman filter can achieve a positioning accuracy of 0.08 m and 0.09 m for T01 in the horizontal and vertical directions and 0.83 m and 0.91 m for T02 in the horizontal and vertical directions, respectively. The accuracy of the velocity and attitude estimations is greatly improved by the introduction of vision.

A Dual w-Test Based Quality Control Algorithm for Integrated IMU/GNSS Navigation in Urban Areas

Integration of the Global Navigation Satellite System (GNSS), with Inertial Measurement Unit (IMU) sensors to improve navigation performance, is widely used in many land-based applications. However, further application, especially in urban areas, is limited by the quality (due mainly to multipath effects) and availability of GNSS measurements, with a significant impact on performance, especially from low grade integration. To maximize the potential of GNSS measurements, this paper proposes a dual w-test-based quality control algorithm for integrated IMU/GNSS navigation in urban areas. Quality control is achieved through fault detection and exclusion (FDE) with the capability to detect simultaneous multiple faults in measurements from different satellites. The remaining fault-free GNSS measurements are fused with IMU sensor measurements to obtain the final improved state solution. The effectiveness of the algorithm is validated in a deep urban field test. Compared to the cases without fault exclusion, the results show improvements of about 24% and 30% in horizontal and vertical positioning components, respectively.

Robust GPS-Vision Localization via Integrity-Driven Landmark Attention

<h3>Abstract</h3> For robust GPS-vision navigation in urban areas, we propose an integrity-driven landmark attention (ILA) technique via stochastic reachability. Inspired by cognitive attention in humans, we perform convex optimization to select a subset of landmarks from GPS and vision measurements that maximizes integrity-driven performance. Given known measurement error bounds in non-faulty conditions, our ILA technique follows a unified approach to address both GPS and vision faults and is compatible with any off-the-shelf estimator. We analyze measurement deviation to estimate the stochastic reachable set of positions associated with each landmark, which is parameterized via probabilistic zonotope (p-zonotope). We apply set union to formulate a p-zonotopic cost that represents the size of position bounds based on landmark inclusion/exclusion. We jointly minimize the p-zonotopic cost and maximize the number of landmarks via convex relaxation. For an urban data set, we demonstrate improved localization accuracy and robust predicted availability for a pre-defined risk and alert limit.

Pseudorange error prediction for adaptive tightly coupled GNSS/IMU navigation in urban areas

Pseudorange error prediction for adaptive tightly coupled GNSS/IMU navigation in urban areas

Resilient multipath prediction and detection architecture for low‐cost navigation in challenging urban areas

A twofold architecture based on GNSS multipath environment prediction and detection is presented in a context of loosely coupled and tightly coupled IMU/GNSS integration for navigation in urban areas. A signal quality monitoring group of techniques is applied for a platform self-contained effort to detect and exclude multipath-contaminated GNSS signals. Additionally, the sensor integration Kalman filter stochastic model is adjusted on-the-fly based on a GNSS multipath environment map. The map is populated by crowdsourcing and contains the spatial distribution of average carrier-to-noise-density ratio measurements, linked to the probability of non-line-of-sight, multipath-contaminated, diffracted, and attenuated signal reception. To address the map availability issue, a random forest machine learning model is developed to propagate the map to the city areas not directly surveyed by the mapping fleet based on open-access geographic data. The architecture performance is evaluated in the automotive scenario showing 13-17% accuracy improvement compared to a conventional Kalman filter.

Multi-Agent Collaborative GNSS/Camera/INS Integration Aided by Inter-Ranging for Vehicular Navigation in Urban Areas

Achieving accurate and reliable positioning in dynamic urban scenarios using low-cost vehicular onboard sensors, such as the global navigation satellite systems (GNSS), camera, and inertial measurement unit (IMU), is still a challenging problem. Multi-Agent collaborative integration (MCI) opens a new window for achieving this goal, by sharing the sensor measurements between multiple agents to further improve the accuracy of respective positioning. One of the major difficulties in MCI is to effectively connect all the sensor measurements arising from multiple independent agents. The popular approach is to find the overlapping areas between agents using active sensors, such as cameras. However, the performance of overlapping area detection is significantly degraded in outdoor urban areas due to the challenges arising from numerous unexpected moving objects and unstable illumination conditions. To fill this gap, this paper proposes to leverage both the camera-based overlapping area detection and the inter-ranging measurements to boost the cross-connection between multi-agents and brings the MCI to outdoor urban scenarios using low-cost onboard sensors. Moreover, a novel MCI framework is proposed to integrate the sensor measurements from the low-cost GNSS receiver, camera, IMU, and inter-ranging using state-of-the-art factor graph optimization (FGO) to fully explore their complementary properties. The proposed MCI framework is validated using two challenging datasets collected in urban canyons of Hong Kong. We conclude that the proposed MCI framework can effectively improve the positioning accuracy of the respective agents in the evaluated datasets. We believe that the proposed MCI framework has the potential to be prevalently adopted by the connected intelligent transportation systems (ITS) applications to provide robust positioning using low-cost onboard sensors in urban scenarios.

Tightly-Coupled GNSS/Vision Using a Sky-Pointing Camera for Vehicle Navigation in Urban Areas.

This paper presents a method of fusing the ego-motion of a robot or a land vehicle estimated from an upward-facing camera with Global Navigation Satellite System (GNSS) signals for navigation purposes in urban environments. A sky-pointing camera is mounted on the top of a car and synchronized with a GNSS receiver. The advantages of this configuration are two-fold: firstly, for the GNSS signals, the upward-facing camera will be used to classify the acquired images into sky and non-sky (also known as segmentation). A satellite falling into the non-sky areas (e.g., buildings, trees) will be rejected and not considered for the final position solution computation. Secondly, the sky-pointing camera (with a field of view of about 90 degrees) is helpful for urban area ego-motion estimation in the sense that it does not see most of the moving objects (e.g., pedestrians, cars) and thus is able to estimate the ego-motion with fewer outliers than is typical with a forward-facing camera. The GNSS and visual information systems are tightly-coupled in a Kalman filter for the final position solution. Experimental results demonstrate the ability of the system to provide satisfactory navigation solutions and better accuracy than the GNSS-only and the loosely-coupled GNSS/vision, 20 percent and 82 percent (in the worst case) respectively, in a deep urban canyon, even in conditions with fewer than four GNSS satellites.

An enhanced MEMS-INS/GNSS integrated system with fault detection and exclusion capability for land vehicle navigation in urban areas

We describe an enhanced quality control algorithm for the MEMS-INS/GNSS integrated navigation system. It aims to maintain the system's reliability and availability during global navigation satellit...

Performance assisted enhancement based on change point detection and Kalman filtering

A performance assisted enhancement Kalman filtering algorithm (PAE-KF) for GPS/INS integration navigation in urban areas was presented in this work. The aim of this PAE-KF algorithm was to prevent “deep contamination” caused by error GPS data. This filtering algorithm effectively combined fault estimation of raw GPS data and nonholonomic constraint of vehicle. In fault estimation, a change point detection algorithm based on abrupt change model was proposed. Statistical tool was then used to infer the future bound of GPS data, which can detect faults in GPS raw data. If any kinds of faults were detected, dead reckoning mechanism begins to compute current position. Nonholonomic constraint condition of vehicle was used to estimate velocity of vehicle and change point detection was added into classic Kalman filtering structure. Experiment on vehicle shows that even when the GPS signals are unavailable for a period of time, this method can also output high accuracy data.

The annotated laser data set for navigation in urban areas

In this paper, we present a challenging data set for use in a variety of mobile robotic navigation tasks. Based on the data set that we released previously, we further extend its capability by annotating the laser range data. Apart from the laser range data, the dead-reckoning trajectory and the tri-camera imagery, the true segmentation differentiating between objects and the true moving object detection (MOD) discriminating moving objects are also annotated. The data set is intended for extensive evaluation of localization, mapping and MOD by providing highly accurate relative pose, object segmentation and MOD. All data are synchronized, carefully annotated and formatted in space-delimited plaintext format. We also provide an interactive web interface to facilitate the access to the data and the annotations.

都市部におけるサブメータ級位置決定の研究

This paper shows a method to achieve a sub-meter level navigation in urban areas. Before the proposed method, the present RTK-GPS performance in urban areas is introduced. It is known that the carrier phase is vulnerable to obstruction and low signal to noise ratio. In order to compensate of the weakness of the carrier phase, the proposed method uses the velocity vector deduced from the Doppler frequency. In this paper, by using both carrier phase and Doppler frequency with reliability check method of these observations, sub-meter level navigation was demonstrated even in the medium urban areas. Furthermore, this paper shows the limitations of GNSS side on other sensors.

Autonomous navigation in urban areas using GIS-managed information

This paper describes the usefulness of a Geographical Information System (GIS) for autonomous navigation of intelligent vehicles. In many urban applications the use of GPS alone is not sufficient and needs to be backed up with Dead-Reckoned (DR) sensors, map data and additional sensors like cameras or laser scanners. Geographical information can be used in two different ways. Firstly, preexisting features of the environment, such as roads, can be used as constraints in localisation space. Secondly, the geographical information can include landmark locations. The use of these two types of data is illustrated by a localisation system for urban areas: a laser scanner detects natural landmarks that are characterised during a learning phase. As the amount of data can be large, we propose a strategy for grouping the laser landmarks in enhanced local maps corresponding to the roads of a GIS layer, through the use of an L1 GPS receiver and DR sensors. Real experiments are reported to illustrate the performance of this approach.

GPS/MEMS INS integrated system for navigation in urban areas

This paper evaluates the performance of a tightly coupled GPS/INS integrated system based on low cost MEMS IMUs in dense urban areas, and investigates two different methods to improve its performance. The first method used is to derive observations from two different constraint equations reflecting the behavior of a typical land vehicle. The first constraint equation is derived assuming that the vehicle does not slip and always remains in contact with the ground. If these assumptions are true the velocity of the vehicle in the plane perpendicular to the forward direction should be zero. The second constraint equation is derived from the fact that the height does not change much in a short time interval in a land vehicular environment. Thus, when a GPS outage occurs (partial/complete), the integrated system combines the INS and constraints-derived virtual measurements to keep the position and velocity errors bounded. This method is suitable for use in real-time applications. The second method is specifically suitable for a post-mission application and involves the use of Rauch-Tung-Striebel (RTS) smoother. The designed system performance is evaluated using two data sets collected in dense urban areas. The obtained results demonstrate the effectiveness of different algorithms considered, in controlling the INS error growth, and indicates the potential of MEMS IMUs for use in land vehicle navigation applications.

Tight integration of digital map and in-vehicle positioning unit for car navigation in urban areas

Now GPS has been widely used for land, sea and air navigation. However, due to signal blockage and severe multipath environments in urban areas, such as in Hong Kong, GPS alone can not satisfy most land vehicle navigation requirements. Dead Reckoning (DR) systems have been widely used to bridge the gaps of GPS and to smooth GPS position errors. However, the DR drift errors increase with time rapidly and frequent calibration is required. Under the normal situation, GPS is sufficient to provide the calibration to the DR unit. However, GPS may not be available in urban areas for more than 20 min, and the DR position errors can reach hundreds of meters during the period. As land vehicles have to be on roads, digital map can be used to constrain the locations of vehicles, known as map-matching. One of the main problems for map-matching techniques is mis-matching, that may be caused by the positioning sensor errors and the complexity of city road network. In this paper, a newly developed model to tightly integrate digital map and in-vehicle positioning unit for car navigation is introduced. With this method, it improves the position accuracy by constraining the vehicle location on the roads. Moreover it provides the close-loop controls for the DR drift errors by feeding back the coordinates of the feature points of the road network and road bearings to the DR unit and therefore the navigation system can be used for longer period when GPS is not available. Extensive tests have been carried out in Hong Kong. It demonstrates that this close-loop approach is much better on the reliability of map-matching, as the positioning sensor errors are constantly calibrated by the digital map.