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 scenarios including high-speed motion, repetitive scenarios, dynamic objects, etc. ECMD provides data from two sets of stereo event cameras with different resolutions (640×480, 346×260), stereo industrial cameras, an infrared camera, a top-installed mechanical LiDAR with two slanted LiDARs, two consumer-level GNSS receivers, and an onboard IMU. Meanwhile, the ground-truth of the vehicle was obtained using a centimeter-level high-accuracy GNSS-RTK/INS navigation system. All sensors are well-calibrated and temporally synchronized at the hardware level, with recording data simultaneously. We additionally evaluate several state-of-the-art SLAM algorithms for benchmarking visual and LiDAR SLAM and identifying their limitations. The dataset is available at https://arclab-hku.github.io/ecmd/ .

Positioning in urban areas is still a challenge due to non-line-of-sight (NLOS) and multipath reception. This paper explores the geometrical characteristics of the GNSS ranging measurement by a graphical representation to better indicate the pseudorange consistency, which can be used to mitigate the NLOS and multipath receptions. The graphical representation is created by the grid-based method combined with the single differenced technique, which is called the single differenced residual map (SDRes Map). With the graphical properties of the SDRes Map, four main focuses of the NLOS/multipath problems, including positioning, signal status prediction, satellite weighting calculation, and NLOS/multipath error calculation, are able to be tackled simultaneously and demonstrated to have superior performance against the conventional or even state-of-the-art method methods.

Smooth and accurate global navigation satellite system (GNSS) positioning for pedestrians in urban canyons is still a challenge due to the multipath effects and the non-line-of-sight (NLOS) receptions caused by the reflections from surrounding buildings. Factor graph optimization (FGO) attracts more and more attention in GNSS society for improving urban GNSS positioning by effectively exploiting the measurement redundancy from historical information to resist the outlier measurements. Unfortunately, the FGO-based GNSS standalone positioning is still challenged in highly urbanized areas. As an extension of the previous FGO-based GNSS positioning method, the potential of the pedestrian dead reckoning (PDR) model in FGO to improve the GNSS standalone positioning performance in urban canyons is exploited in this paper. Specifically, the relative motion of the pedestrian is estimated based on the raw acceleration measurements from the onboard smartphone inertial measurement unit (IMU) via the PDR algorithm. Then the raw GNSS pseudorange, Doppler measurements, and relative motion from PDR are integrated using the FGO. Given the context of pedestrian navigation with a small acceleration most of the time, a novel soft motion model is proposed to smooth the states involved in the factor graph model. This paper verified the effectiveness of employing the PDR model in FGO step-by-step through two datasets collected in dense urban canyons of Hong Kong using smartphone-level GNSS receivers. The comparison between the conventional extended Kalman filter, several existing methods, and FGO-based integration is presented. The proposed method shows better results than the conventional FGO method in all test datasets, with at least a 22% decrease in the mean value of positioning error. The proposed method reduces the average localization error from 31.64 m to 18.51 m in a deep urban area.

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 accuracy, particularly suited for the intricate layouts of urban canyons. Our approach integrates the prior map with lane line information into the Factor Graph Optimization (FGO) algorithm, effectively mitigating the impacts of multi-path effects and NLOS. However, the poor accuracy of the initial guess from the GNSS positioning can easily lead to incorrect lane matching which is one of the main challenges of lane matching in highly urbanized areas. To fill this gap, this paper proposes to use the switchable factor to model the potential incorrect lane matching by leveraging the redundancy of lane information across multi-epochs. This paper verified the effectiveness of this methodology using two datasets from the dense urban environment of Hong Kong, collected using the low-cost automobile level receiver, and compared the results with conventional methods. Our findings affirm that integrating the FGO-based GNSS positioning system with map information significantly boosts positioning accuracy, demonstrating the robustness of our approach.

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 model of weighting observations is not suitable in urban canyons. This paper addresses these challenges by integrating Artificial Intelligence (AI), specifically deep learning, into GNSS positioning process to enhance positioning accuracy. Traditional methods have primarily focused on pseudorange correction due to the absence of ground truth for weight estimation. In response, we propose an innovative indirect training approach using deep learning to optimize both pseudorange bias and weight estimation, aiming to minimize the positioning errors. To support this integration, we developed pyrtklib, a Python binding for the open-source RTKLIB tool, bridging the gap between traditional GNSS algorithms, typically developed in Fortran or C, and modern Python-based AI frameworks. Comparative analyses demonstrate that our method surpasses established tools like goGPS and RTKLIB in positioning accuracy, marking a significant advancement in the field. The source code of tightly coupled deep learning and GNSS integration, along with pyrtklib, is available on GitHub at https://github.com/ebhrz/TDL-GNSS and https://github.com/IPNL-POLYU/pyrtklib.

A sharp yet conservative overbound for heavy-tailed error distributions is essential in integrity monitoring applications due to availability and continuity constraints. This paper proposes the Principal Gaussian overbound (PGO) for heavy-tailed error distributions by leveraging the characteristics of the Gaussian mixture model. The overbounding property of the PGO is proved to be preserved through convolution, which makes it possible to derive pseudorange-level requirements from the position domain integrity requirements. Experimental results on two datasets show that the PGO provides the most competitive bounding performance for heavy-tailed differential global navigation satellite system (DGNSS) pseudorange errors when compared to the two-step Gaussian overbound and Gaussian-Pareto overbound, yielding a sharp bound in both the core and tail parts of the error distribution. The proposed method reduces the mean vertical protection level (VPL) by more than 78% compared to the two-step Gaussian overbounding method on the urban dataset. In addition, the mean computation time of VPL is only 0.08 _S with fifteen measurements by employing fast Fourier transforms, suggesting the substantial potential of the PGO in GNSS applications with strict integrity and real-time requirements. Furthermore, the feasibility of the PGO in fault detection is discussed.