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Found 7 papers, 4 papers with code
COVINS-G: A Generic Back-end for Collaborative Visual-Inertial SLAM
The paradigm of a centralized architecture is well established, with the robots (i. e. agents) running Visual-Inertial Odometry (VIO) onboard while communicating relevant data, such as e. g. Keyframes (KFs), to a central back-end (i. e. server), which then merges and optimizes the joint maps of the agents.
COVINS: Visual-Inertial SLAM for Centralized Collaboration
Collaborative SLAM enables a group of agents to simultaneously co-localize and jointly map an environment, thus paving the way to wide-ranging applications of multi-robot perception and multi-user AR experiences by eliminating the need for external infrastructure or pre-built maps.
Hough2Map -- Iterative Event-based Hough Transform for High-Speed Railway Mapping
As a basis for a localization system we propose a complete on-board mapping pipeline able to map robust meaningful landmarks, such as poles from power lines, in the vicinity of the vehicle.
Distributed Variable-Baseline Stereo SLAM from two UAVs
VIO has been widely used and researched to control and aid the automation of navigation of robots especially in the absence of absolute position measurements, such as GPS.
Aerial Single-View Depth Completion with Image-Guided Uncertainty Estimation
In this paper, we propose a depth completion and uncertainty estimation approach that better handles the challenges of aerial platforms, such as large viewpoint and depth variations, and limited computing resources.
A Fully-Integrated Sensing and Control System for High-Accuracy Mobile Robotic Building Construction
We present a fully-integrated sensing and control system which enables mobile manipulator robots to execute building tasks with millimeter-scale accuracy on building construction sites.
Learning Deep Descriptors With Scale-Aware Triplet Networks
Research on learning suitable feature descriptors for Computer Vision has recently shifted to deep learning where the biggest challenge lies with the formulation of appropriate loss functions, especially since the descriptors to be learned are not known at training time.
