Related papers: CredibleDFGO: Differentiable Factor Graph Optimiza…
Reliable positioning in dense urban environments remains challenging due to frequent GNSS signal blockage, multipath, and rapidly varying satellite geometry. While factor graph optimization (FGO)-based GNSS-IMU fusion has demonstrated…
Convolutional neural networks (CNNs) are currently among the most widely-used deep neural network (DNN) architectures available and achieve state-of-the-art performance for many problems. Originally applied to computer vision tasks, CNNs…
Robust navigation in urban environments has received a considerable amount of both academic and commercial interest over recent years. This is primarily due to large commercial organizations such as Google and Uber stepping into the…
The execution of flight missions by unmanned aerial vehicles (UAV) primarily relies on navigation. In particular, the navigation pipeline has traditionally been divided into positioning and control, operating in a sequential loop. However,…
This study proposes a centimeter-accurate positioning method that utilizes a Rao-Blackwellized particle filter (RBPF) without requiring integer ambiguity resolution in global navigation satellite system (GNSS) carrier phase measurements.…
In urban environments, where line-of-sight signals from GNSS satellites are frequently blocked by high-rise objects, GNSS receivers are subject to large errors in measuring satellite ranges. Heuristic methods are commonly used to estimate…
Accurate localization is a core component of a robot's navigation system. To this end, global navigation satellite systems (GNSS) can provide absolute measurements outdoors and, therefore, eliminate long-term drift. However, fusing GNSS…
In this paper we develop a novel method of combining many forecasts based on a machine learning algorithm called Graphical LASSO (GL). We visualize forecast errors from different forecasters as a network of interacting entities and…
Multi-sensor fusion is an effective way to enhance the positioning performance of autonomous underwater vehicles (AUVs). However, underwater multi-sensor fusion faces challenges such as heterogeneous frequency and dynamic availability of…
Gravitational waves (GWs) can be distorted by intervening mass distributions while propagating, leading to frequency-dependent modulations that imprint a distinct signature on the observed waveforms. Bayesian inference for GW lensing with…
An essential function for automated vehicle technologies is accurate localization. It is difficult, however, to achieve lane-level accuracy with low-cost Global Navigation Satellite System (GNSS) receivers due to the biased noisy…
Multivariate time series (MTS) forecasting has shown great importance in numerous industries. Current state-of-the-art graph neural network (GNN)-based forecasting methods usually require both graph networks (e.g., GCN) and temporal…
High-level robotic manipulation tasks demand flexible 6-DoF grasp estimation to serve as a basic function. Previous approaches either directly generate grasps from point-cloud data, suffering from challenges with small objects and sensor…
Cross-view geo-localization (CVGL) between drone and satellite imagery remains challenging due to severe viewpoint gaps and the presence of hard negatives, which are visually similar but geographically mismatched samples. Existing mining or…
In this work, we propose a factor graph optimization (FGO) framework to simultaneously solve the calibration problem for Ultra-WideBand (UWB) anchors and the robot localization problem. Calibrating UWB anchors manually can be time-consuming…
Accurate, continuous, and reliable positioning is a critical component of achieving autonomous driving. However, in complex urban canyon environments, the vulnerability of a stand-alone sensor and non-line-of-sight (NLOS) caused by high…
Accurate and fast localization is vital for safe autonomous navigation in GPS-denied areas. Fine-Grained Cross-View Geolocalization (FG-CVG) aims to estimate the precise 2-Degree-of-Freedom (2-DoF) location of a ground image relative to a…
A crucial function for automated vehicle technologies is accurate localization. Lane-level accuracy is not readily available from low-cost Global Navigation Satellite System (GNSS) receivers because of factors such as multipath error and…
Multivariate time series (MTS) forecasting is an essential problem in many fields. Accurate forecasting results can effectively help decision-making. To date, many MTS forecasting methods have been proposed and widely applied. However,…
Cooperative localization is essential for swarm applications like collaborative exploration and search-and-rescue missions. However, maintaining real-time capability, robustness, and computational efficiency on resource-constrained…