11 accepted papers
Position tracking based on bearing measurements and Ultra-wideband (UWB) ranging is widely used in robotic navigation tasks. However, due to variations in the number of robots, anchor configurations, UWB tag layouts, and the presence or absence of anonymous visual observations, existing methods typi…
X-ray to CT registration is an essential technique to provide on-site guidance for clinicians and medical robots by aligning preoperative information with intraoperative images. Current methods focus on local registration with small capture ranges and necessitate a manual initial alignment before pr…
With robots being deployed in increasingly complex environments like underground mines and planetary surfaces, the multi-sensor fusion method has gained more and more attention which is a promising solution to state estimation in the such scene. The fusion scheme is a central component of these meth…
Drift-free localization is essential for autonomous vehicles. In this letter, we address the problem by proposing a filter-based framework, which integrates the visual-inertial odometry and the measurements from the pre-built map. In this framework, the transformation between the odometry frame and
In recent years, Visual-Inertial Odometry (VIO) has achieved many significant progresses. However, VIO meth-ods suffer from localization drift over long trajectories. In this paper, we propose a First-Estimates Jacobian Visual-Inertial-Ranging Odometry (FEJ-VIRO) to reduce the localization drifts of…
One primary difficulty preventing the visual localization for service robots is the robustness against changes, including environmental changes and perspective changes. In recent years, learning-based feature matching methods have been widely studied and effectively verified in practical application
Global point cloud registration is an essential module for localization, of which the main difficulty exists in estimating the rotation globally without initial value. With the aid of gravity alignment, the degree of freedom in point cloud registration could be reduced to 4DoF, in which only the hea…
Global localization and kidnapping are two challenging problems in robot localization. The popular method, Monte Carlo Localization (MCL) addresses the problem by iteratively updating a set of particles with a “sampling-weighting” loop. Sampling is decisive to the performance of MCL [1]. However, tr
Visual localization for planar moving robot is important to various indoor service robotic applications. To handle the textureless areas and frequent human activities in indoor environments, a novel robust visual localization algorithm which leverages dense correspondence and sparse depth for planar…
Map based visual inertial localization is a crucial step to reduce the drift in state estimation of mobile robots. The underlying problem for localization is to estimate the pose from a set of 3D-2D feature correspondences, of which the main challenge is the presence of outliers, especially in chang…
Visual localization has attracted considerable attention due to its low-cost and stable sensor, which is desired in many applications, such as autonomous driving, inspection robots and unmanned aerial vehicles. However, current visual localization methods still struggle with environmental changes ac…