On the representation and estimation of spatial uncertainly
International Journal of Robotics Research
Estimating uncertain spatial relationships in robotics
Autonomous robot vehicles
Robust Kalman Filtering for Signals and Systems with Large Uncertainties
Robust Kalman Filtering for Signals and Systems with Large Uncertainties
Simultaneous Localization and Map-Building Using Active Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Real-Time Simultaneous Localisation and Mapping with a Single Camera
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Vision-Based Multi-Robot Simultaneous Localization and Mapping
CRV '04 Proceedings of the 1st Canadian Conference on Computer and Robot Vision
Robocentric map joining: Improving the consistency of EKF-SLAM
Robotics and Autonomous Systems
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Robust extended Kalman filtering
IEEE Transactions on Signal Processing
H∞ nonlinear filtering of discrete-time processes
IEEE Transactions on Signal Processing
Convergence and Consistency Analysis for Extended Kalman Filter Based SLAM
IEEE Transactions on Robotics
Survey of advances in guidance, navigation, and control of unmanned rotorcraft systems
Journal of Field Robotics
Bearing-only visual SLAM for small unmanned aerial vehicles in GPS-denied environments
International Journal of Automation and Computing
Journal of Intelligent and Robotic Systems
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This paper aims to present a robust airborne 3D Visual Simultaneous Localization and Mapping (VSLAM) solution based on a stereovision system. We propose three innovative contributions to the Airborne VSLAM. The first one is the development of an alternative data fusion nonlinear H∞ filtering scheme. This scheme is based on 3D vision observation model and avoids issues linked with the classical Extended Kalman Filtering (EKF) techniques such as the linearization errors, the initialization problem and noise statistics assumptions. The second contribution consists of a consistency and observability analysis for the Airborne VSLAM. The third contribution is a new approach to map management, based on the k-nearest landmark concept, and allowing efficient loop closure detection and map building. This approach reduces considerably the complexity of our Airborne VSLAM algorithm, which becomes independent of the map landmark number. Simulation results show the efficiency of the proposed Airborne VSLAM solution for which comparisons with other techniques are favourable.