Simultaneous Localization and Map-Building Using Active Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
FastSLAM: a factored solution to the simultaneous localization and mapping problem
Eighteenth national conference on Artificial intelligence
Speeded-Up Robust Features (SURF)
Computer Vision and Image Understanding
Stereo vision specific models for particle filter-based SLAM
Robotics and Autonomous Systems
Vision-based global localization for mobile robots with hybrid maps of objects and spatial layouts
Information Sciences: an International Journal
Bridging the gap between feature- and grid-based SLAM
Robotics and Autonomous Systems
Image-based homing navigation with landmark arrangement matching
Information Sciences: an International Journal
Performance evaluation of 1-point-RANSAC visual odometry
Journal of Field Robotics
Inverse Depth Parametrization for Monocular SLAM
IEEE Transactions on Robotics
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The problem of Simultaneous Localization and Mapping (SLAM) is essential in mobile robotics. The obtention of a feasible map of the environment poses a complex challenge, since the presence of noise arises as a major problem which may gravely affect the estimated solution. Consequently, a SLAM algorithm has to cope with this issue but also with the data association problem. The Extended Kalman Filter (EKF) is one of the most traditionally implemented algorithms in visual SLAM. It linearizes the movement and the observation model to provide an effective online estimation. This solution is highly sensitive to non-linear observation models as it is the omnidirectional visual model. The Stochastic Gradient Descent (SGD) emerges in this work as an offline alternative to minimize the non-linear effects which deteriorate and compromise the convergence of traditional estimators. This paper compares both methods applied to the same approach: a navigation robot supported by an efficient map model, established by a reduced set of omnidirectional image views. We present a series of real data experiments to assess the behavior and effectiveness of both methods in terms of accuracy, robustness against errors and speed of convergence.