Related papers: Optical flow GNSS for navigation in the Indian sub…
In this article we model a Global Navigation Satellite System (GNSS) in a Schwarzschild space-time, as a first approximation of the relativistic geometry around the Earth. The closed time-like and scattering light-like geodesics are…
In the domain of computer vision, optical flow stands as a cornerstone for unraveling dynamic visual scenes. However, the challenge of accurately estimating optical flow under conditions of large nonlinear motion patterns remains an open…
Interpreting motion captured in image sequences is crucial for a wide range of computer vision applications. Typical estimation approaches include optical flow (OF), which approximates the apparent motion instantaneously in a scene, and…
An algorithm based on Artificial Neural Networks is proposed in this paper to improve the accuracy of Inertial Navigation System (INS)/ Global Navigation Satellite System (GNSS) integrated navigation during the absence of GNSS signals. The…
We present a self-supervised approach to estimate flow in camera image and top-view grid map sequences using fully convolutional neural networks in the domain of automated driving. We extend existing approaches for self-supervised optical…
Multipath and non-line-of-sight (NLOS) signals are the major causes of poor accuracy of a global navigation satellite system (GNSS) in urban areas. Despite the wide usage of the GNSS in populated urban areas, it is difficult to suggest a…
Gaussian splatting has recently gained traction as a compelling map representation for SLAM systems, enabling dense and photo-realistic scene modeling. However, its application to monocular SLAM remains challenging due to the lack of…
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…
Autonomous vehicle navigation is a key challenge in artificial intelligence, requiring robust and accurate decision-making processes. This research introduces a new end-to-end method that exploits multimodal information from a single…
Combining Global Navigation Satellite System (GNSS) with visual and inertial sensors can give smooth pose estimation without drifting. The fusion system gradually degrades to Visual-Inertial Odometry (VIO) with the number of satellites…
Real-time high-accuracy optical flow estimation is a crucial component in various applications, including localization and mapping in robotics, object tracking, and activity recognition in computer vision. While recent learning-based…
Unlike loose coupling approaches and the EKF-based approaches in the literature, we propose an optimization-based visual-inertial SLAM tightly coupled with raw Global Navigation Satellite System (GNSS) measurements, a first attempt of this…
Optical flow estimation is a fundamental problem in computer vision, yet the reliance on expensive ground-truth annotations limits the scalability of supervised approaches. Although unsupervised and semi-supervised methods alleviate this…
At modern construction sites, utilizing GNSS (Global Navigation Satellite System) to measure the real-time location and orientation (i.e. pose) of construction machines and navigate them is very common. However, GNSS is not always…
Optical flow and disparity are two informative visual features for autonomous driving perception. They have been used for a variety of applications, such as obstacle and lane detection. The concept of "U-V-Disparity" has been widely…
Recent works on optical flow estimation use neural networks to predict the flow field that maps positions of one image to positions of the other. These networks consist of a feature extractor, a correlation volume, and finally several…
Prior art has proposed a secondary application for Global Navigation Satellite System (GNSS) infrastructure for remote sensing of ground-based and maritime targets. Here, a passive radar receiver is deployed to detect uncooperative targets…
Robust, high-precision global localization is fundamental to a wide range of outdoor robotics applications. Conventional fusion methods use low-accuracy pseudorange based GNSS measurements ($>>5m$ errors) and can only yield a coarse…
In recent years, with the development of the Global Navigation Satellite System (GNSS), the satellite navigation technology has played a crucial role in smartphone navigation. To solve the problem of the low positioning accuracy in the…
A global navigation satellite system (GNSS) is a sensor that can acquire 3D position and velocity in an earth-fixed coordinate system and is widely used for outdoor position estimation of robots and vehicles. Various GNSS/inertial…