Weisong Wen

• Doctor of Philosophy
• Professor (Assistant) at The Hong Kong Polytechnic University

Fault detection is crucial to ensure the reliability of localization systems. However, conventional fault detection methods usually assume that noises in the system have a Gaussian distribution, limiting their effectiveness in real-world applications. This study proposes a fault detection algorithm for an extended Kalman filter (EKF)-based localiza...

Pedestrian location tracking in emergency responses and environmental surveys of indoor scenarios tend to rely only on their own mobile devices, reducing the usage of external services. Low-cost and small-sized inertial measurement units (IMU) have been widely distributed in mobile devices. However, they suffer from high-level noises, leading to dr...

In the field of autonomous driving, the micro-electromechanical systems (MEMS)-based vehicle navigation usually adopts multi-sensor integrated navigation to achieve high-precision positioning. However, due to the complex environments, the accuracy and reliability of navigation sensors may be significantly reduced. To address these challenges, the i...

Smartphones, as ubiquitous consumer electronics de-vices, rely heavily on Global Navigation Satellite Systems (GNSS) for various applications, including navigation and location-based services. However, the small-sized and low-cost patch antennas used in smartphones are particularly susceptible to multipath ef-fects and signal degradation, posing si...

Global Navigation Satellite Systems (GNSS) are crucial for intelligent transportation systems (ITS), providing essential positioning capabilities globally. However, in urban canyons, the GNSS performance could significantly degraded due to the blockage of direct GNSS signals. The pseudorange measurements are largely affected and the conventional mo...

Precise dynamic model calibration is essential in achieving reliable control of unmanned aerial vehicles (UAV). However, the existing methods tend to use simplified dynamic models and cannot adapt to variations of the dynamic model. To fill this gap, this paper explores an online dynamic model calibration (ODMC) method for the quadrotor based on fa...

The performance of smartphone-based fingerprinting indoor positioning methods heavily depends on the database quality and matching algorithms, typically providing only 2D coordinates. Traditional methods also face challenges like labor-intensive data collection, limited accuracy, and reduced longterm reliability. This paper proposes a Self-Attentio...

Pedestrian dead reckoning (PDR) using smartphones is a popular method for indoor localization. However, it encounters challenges due to the drift of position errors. While external resources like Wi-Fi, Bluetooth, and indoor maps can correct the drift, they require the pre-installation of facilities or information, limiting their application. Inspi...

This paper explores the pervasive challenges of pedestrian positioning using smartphones in densely populated urban environments where Global Navigation Satellite System (GNSS) signals are inaccessible, for example, in indoor areas. Existing sensor-based positioning methods, such as inertial navigation systems (INS), GNSS, and visual-inertial odome...

Indoor localization for pedestrians, which relies solely on inertial odometry, has been a topic of great interest. Its significance lies in its ability to provide positioning solutions independently, without the need for external data. Although traditional strap-down inertial navigation shows rapid drift, the introduction of pedestrian dead reckoni...

Artificial intelligence (AI) is revolutionizing numerous fields, with increasing applications in Global Navigation Satellite Systems (GNSS) positioning algorithms in intelligent transportation systems (ITS) via deep learning. However, a significant technological disparity exists as traditional GNSS algorithms are often developed in Fortran or C, co...

A lightweight, high‐definition vector map (HDVM) enables fully autonomous vehicles. However, the generation of HDVM remains a challenging problem, especially in complex urban scenarios. Moreover, numerous factors in the urban environment can degrade the accuracy of HDVM, necessitating a reliable error quantification. To address these challenges, th...

Reliable and precise information pertaining to the position, velocity, and attitude is essential for automated driving. This paper proposes FGO-MFI, a cost-effective and robust multi-sensor fusion and integration localization framework that utilizes factor graph optimization. Firstly, a tightly coupled Global Navigation Satellite Systems (GNSS)/on-...

Autonomous vehicles are widely used in logistics, public transportation, and specialized industries, its high-precision navigation and positioning is predominantly supported through strapdown inertial navigation system/global navigation satellite system (SINS/GNSS) integrated navigation, thus ensuring safe and efficient operations. In practice, GNS...

Fault detection for localization systems with non-Gaussian measurement noises is a challenging task. This paper investigates the impacts of noise modeling on fault detection performance in the inertial measurement units (IMU) and light detection and ranging (LiDAR) integrated localization system based on the extended Kalman filter (EKF). Specifical...

LiDAR-based localization is valuable for applications like mining surveys and underground facility maintenance. However, existing methods can struggle when dealing with uninformative geometric structures in challenging scenarios. This paper presents RELEAD, a LiDAR-centric solution designed to address scan-matching degradation. Our method enables d...

Collaborative state estimation using heterogeneous multi-sensors is a fundamental prerequisite for robotic swarms operating in GPS-denied environments, presenting a formidable research challenge. In this paper, we introduce a centralized system to facilitate collaborative LiDAR-ranging-inertial state estimation in expansive environments, enabling r...

The Global Navigation Satellite System (GNSS) is one of the most popular solutions to localize potential road cracks. Unfortunately, the accurate positioning of the GNSS in urban environments presents a significant challenge due to complex signal blockage and reflection phenomena. To tackle this, we propose a method that enhances GNSS positioning a...

Reliable and cost-effective localization is of great importance for the realization of intelligent vehicles (IV) in complex scenes. The visual-inertial odometry (VIO) can provide high-frequency position estimation over time but is subjected to drift over time. Recently developed map-aided VIO opens a new window for drift-free visual localization bu...

Global navigation satellite system (GNSS) positioning is essential for achieving absolute vehicular positioning in urban scenarios; however, it suffers from limited measurement redundancy and substantial faults caused by complex urban environments. In this work, we propose the subspace-based adaptive error modeling and fault detection and exclusion...

Urban pedestrian navigation is a challenging issue as the most popular positioning source, the Global Navigation Satellite Systems (GNSS), is severely affected by signal reflections or blockages from high-rise buildings. Unlike the GNSS, the inertial measurement unit (IMU) is less sensitive to environmental conditions but is, unfortunately, subject...

Safety-quantifiable and accurate localization is of great importance for safety-critical applications with navigation requirements, such as intelligent vehicles (IV). LiDAR-based localization with the prior map is highly expected due to its high accuracy. However, how to reliably quantify the safety (quantify the maximum potential localization erro...

Navigating to a destination using smartphones has recently become a primary way in people’s daily lives. Positioning is the prerequisite condition before efficient path planning can be achieved. In urban environments, people can self-localize with the aid of the global navigation satellite system (GNSS). However, last-mile navigation is still a cha...

The use of mobile devices for indoor localization has proven to be a convenient solution for pedestrians in Internet of Things (IoT) applications. Radiofrequency (RF) signals, including Wi-Fi, Bluetooth, and others, are among the most commonly used sources. However, their availability cannot be guaranteed in all scenarios. Although pedestrian dead...

Autonomous surface ships have become increasingly interesting for commercial maritime sectors. Before deep learning (DL) was proposed, surface ship autonomy was mostly model-based. The development of artificial intelligence (AI) has prompted new challenges in the maritime industry. A detailed literature study and examination of DL applications in a...

Accurate and reliable localization is of great importance for autonomous vehicles (AV). Mainstream localization approaches in autonomous vehicles (AV) are limited by the reliability of onboard sensors, which could be vulnerable to sensor failure, such as signal outages of the camera and signal spoofing of the global navigation satellite systems (GN...

Localization plays a vital role in various autonomous systems, providing essential information for perception and planning tasks. However, mainstream localization methods are based on the sensors approach, which is vulnerable in some extreme conditions where sensors probably fail in a short period, such as the camera-based visual positioning. This...

Vehicle dynamic models are the basis of various navigation algorithms in autonomous mobile robots (AMRs), describing the vehicle motion purely by physical law. However, its simplifications on the system complexity and assumptions on the environments prevent it from providing accurate positioning results. Instead of introducing sensors to correct it...

Accurate and reliable positioning is of great importance for the realization of intelligent vehicles (IV). Factor graph optimization (FGO) has been popularized in the field of robotics for state estimation. As a ubiquitous sensor, the IMU is widely used for vehicular positioning based on the preintegration theory using FGO. However, the existing pr...

Intelligent vehicles demand reliable, continuous, and accurate positioning capability. Light Detection and Ranging (LiDAR)-inertial odometry (LIO) provides precise continuous relative pose estimation but suffers from drift over large-scale operations. Global navigation satellite system (GNSS) offers drift-free absolute positioning capability but th...

Dynamic object detection from point clouds has been widely studied in recent years to achieve accurate and robust LiDAR odometry for autonomous driving. Satisfactory accuracy can be achieved by Dynamic object detection from point clouds has been widely studied in recent years to achieve accurate and robust LiDAR odometry for autonomous driving. Sat...

Leveraging multiple sensors enhances complex environmental perception and increases resilience to varying luminance conditions and high-speed motion patterns, achieving precise localization and mapping. This paper proposes, ECMD, an event-centric multisensory dataset containing 81 sequences and covering over 200 km of various challenging driving sc...

Accurate and smooth global navigation satellite system (GNSS) positioning for pedestrians in urban canyons is still a challenge due to the multipath effects and the non-light-of-sight (NLOS) receptions caused by the reflections from surrounding buildings. The recently developed factor graph optimization (FGO) based GNSS positioning method opened a...

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-s...

Robust and precise localization is essential for an autonomous system with navigation requirements. Lidar odometry (LO) has been extensively studied in the past decades to realize this goal. Satisfactory accuracy can be achieved in scenarios with abundant environmental features using existing LO algorithms. Unfortunately, the performance of the LO...

This paper proposes a 3D LiDAR aided global navigation satellite system (GNSS) non-line-of-sight (NLOS) mitigation method due to both static buildings and dynamic objects. A sliding window map describing the environment of the ego-vehicle is first generated, based on real-time 3D point clouds from a 3D LiDAR sensor. Subsequently, the NLOS reception...

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 paper proposes to explo...

A low‐cost and accurate positioning solution is significant for the massive deployment of fully autonomous driving vehicles (ADV). Conventional mechanical LiDAR has proven its performance, but its high cost hinders the massive production of autonomous vehicles. This paper proposes a low‐cost LiDAR/inertial‐based localization solution for autonomous...

This article 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 which only conside...

Global Navigation Satellite System Real-time Kinematic (GNSS-RTK) is an indispensable source for the absolute positioning of autonomous systems. Unfortunately, the performance of the GNSS-RTK is significantly degraded in urban canyons, due to the notorious multipath and Non-Line-of-Sight (NLOS). On the contrary, LiDAR/inertial odometry (LIO) can pr...

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 a...

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. Ins...

In this paper, we proposed a graduated non-convexity (GNC) aided outlier mitigation method for the improvement of the visual-inertial integrated navigation system (VINS) to face the challenge of dynamic environments with numerous unexpected outlier measurements. A GNC optical flow algorithm was proposed for the detection of the outliers of feature...

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 con...

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. Ins...

Accurate positioning and mapping are significant for autonomous systems with navigation requirements. In this paper, a coarse-to-fine loosely-coupled (LC) LiDAR-inertial odometry (LC-LIO) that could explore the complementariness of LiDAR and inertial measurement unit (IMU) was proposed for the real-time and accurate pose estimation of a ground vehi...

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...

Factor graph optimization (FGO) recently has attracted attention as an alternative to the extended Kalman filter (EKF) for GNSS-INS integration. This study evaluates both loosely and tightly coupled integrations of GNSS code pseudorange and INS measurements for real-time positioning, using both conventional EKF and FGO with a dataset collected in a...

Robust and precise localization is essential for the autonomous system with navigation requirements. Light detection and ranging (LiDAR) odometry is extensively studied in the past decades to achieve this goal. Satisfactory accuracy can be achieved in scenarios with abundant environmental features using existing LiDAR odometry (LO) algorithms. Unfo...

LiDAR odometry algorithms are extensively studied for vehicular positioning. However, achieving high-precision positioning using low-cost 16-channel LiDAR in urban canyons remains a challenging problem due to the limited point cloud density from low-cost LiDAR and excessive dynamic surrounding objects. To fill this gap, this paper proposes enrichin...

LiDAR-based SLAM algorithms are extensively studied to providing robust and accurate positioning for autonomous driving vehicles (ADV) in the past decades. Satisfactory performance can be obtained using high-grade 3D LiDAR with 64 channels, which can provide dense point clouds. Unfortunately, the high price significantly prevents its extensive comm...

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 s...

Positioning is a key function for autonomous vehicles that requires globally referenced localization information. Lidar-based mapping, which refers to simultaneous localization and mapping (SLAM), provides continuous positioning in diverse scenarios. However, SLAM error can accumulate through time. Besides, only relative positioning is provided by...

Robust and globally‐referenced positioning is indispensable for autonomous driving vehicles. Global navigation satellite system (GNSS) is still an irreplaceable sensor. Satisfactory accuracy (about 1 m) can be obtained in sparse areas. However, the GNSS positioning error can be up to 100 m in dense urban areas due to the multipath effects and non‐l...

There is an increasing demand for accurate and robust positioning in many application domains, such as the unmanned aerial vehicle (UAV) and autonomous driving vehicles (ADV). The integration of visual odometry and inertial navigation system (INS) is extensively studied to fulfill the positioning requirement. The visual odometry can provide aided p...

GNSS positioning is strongly challenged in urban canyon areas. The signal reflection induces multipath and non-light-of-sight (NLOS). These signal blockages and reflections are caused by the obstacles of signal transmission between the satellites and receiver. The obstacles can be buildings, trees and even a high-rise vehicle such as double-decker...

This chapter focuses on the multi-sensor integrated positioning method for vehicular navigation. The most well-known and public-used positioning technology is global navigation satellite system (GNSS). The GNSS provides globally referenced positioning seivice under allweather conditions throughout the years. It easily obtains a positioning performa...

The visual-inertial integrated navigation system (VINS) has been extensively studied over the past decades to provide accurate and low-cost positioning solutions for autonomous systems. Satisfactory performance can be obtained in an ideal scenario with sufficient and static environment features. However, there are usually numerous dynamic objects i...

The recent development in vehicle-to-everything (V2X) communication opens a new opportunity to improve the positioning performance of the road users. We explore the benefit of connecting the raw data of the global navigation satellite system (GNSS) from the agents. In urban areas, GNSS positioning is highly degraded due to signal blockage and refle...

The integration of the global navigation satellite system (GNSS) and inertial navigation systems (INS) is extensively studied in the past decades for vehicular navigations, such as unmanned aerial vehicles (UAV) and autonomous driving vehicles (ADV). Conventionally, the two most common integration solutions are the loosely-coupled and the tightly-c...

Performing precise positioning is still challenging for autonomous driving. Global navigation satellite system (GNSS) performance can be significantly degraded due to the non-line-of-sight (NLOS) reception. Recently, the studies of 3D building model aided (3DMA) GNSS positioning show promising positioning improvements in urban canyons. In this stud...

Visual-inertial integrated navigation system (VINS) has been extensively studied over the past decades to provide accurate and low-cost positioning solutions for autonomous systems. Satisfactory performance can be obtained in an ideal scenario with sufficient and static environment features. However, there are usually numerous dynamic objects in de...