1/4/2024 0 Comments Poser pro 2012 academic![]() Bags of Binary Words for Fast Place Recognition in Image Sequences. Loop closure: Dorian Gálvez-López and Juan D. ![]() iSAM2: Incremental Smoothing and Mapping Using the Bayes Tree. Global map powered by iSAM2: Michael Kaess, Hordur Johannsson, Richard Roberts, Viorela Ila, John Leonard, Frank Dellaert.Keyframe-based visual–inertial odometry using nonlinear optimization. Ceres-based optimization backend: Stefan Leutenegger, Simon Lynen, Michael Bosse, Roland Siegwart, Paul Timothy Furgale.Active Exposure Control for Robust Visual Odometry in HDR Environments. Brightness/exposure compensation: Zichao Zhang, Christian Forster, Davide Scaramuzza.Benefit of Large Field-of-View Cameras for Visual Odometry. Fisheye/catadioptric camera extension: Zichao Zhang, Henri Rebecq, Christian Forster, Davide Scaramuzza.bibtexĪdditionally, please cite the following papers for the specific extensions you make use of: SVO: Semi-Direct Visual Odometry for Monocular and Multi-Camera Systems. Christian Forster, Zichao Zhang, Michael Gassner, Manuel Werlberger, Davide Scaramuzza.SVO: Fast Semi-Direct Monocular Visual Odometry. Christian Forster, Matia Pizzoli, Davide Scaramuzza.If you use the code in the academic context, please cite: The visual-inertial backend is modified from OKVIS, and the license is retained at the beginning of the related files. For commercial use, please contact sdavide ifi uzh ch. We hope that the efforts we made can facilitate the research and applications of SLAM and spatial perception. SVO Pro and its extensions have been used to support various projects at RPG, such as our recent work on multiple camera SLAM, voxel map for visual SLAM and the tight-coupling of global positional measurements into VIO. Pose graph optimization is also included as a lightweight replacement of the global bundle adjustment.Īn example of the visual-inertial SLAM pipeline on EuRoC dataset is below (green points - sliding window blue points - iSAM2 map): Visual-inertial SLAM with loop closure: Loop closures, via DBoW2, are integrated in the global bundle adjustment.The global map is updated in real-time, thanks to iSAM2, and used for localization at frame-rate. Visual-inertial SLAM: SVO frontend + visual-inertial sliding window optimization backend + globally bundle adjusted map (using iSAM2).Visual-inertial odometry: SVO fronted + visual-inertial sliding window optimization backend (modified from OKVIS).It also includes active exposure control. Visual-odometry: The most recent version of SVO that supports perspective and fisheye/catadioptric cameras in monocular or stereo setup.In summary, this repository offers the following functionalities: SVO Pro features the support of different camera models, active exposure control, a sliding window based backend, and global bundle adjustment with loop closure. Since then, different extensions have been integrated through various research and industrial projects. SVO was born as a fast and versatile visual front-end as described in the SVO paper (TRO-17). This repo includes SVO Pro which is the newest version of Semi-direct Visual Odometry (SVO) developed over the past few years at the Robotics and Perception Group (RPG).
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