OpenVINS 2.6 - EurocMav Dynamic Initialization Demonstration скачать в хорошем качестве

OpenVINS 2.6 - EurocMav Dynamic Initialization Demonstration

The OpenVINS system running on the three EurocMav datasets. The system is manually restarted at random points in each to demonstrate the ability to initialize the system from non-zero velocity conditions. It is relatively robust, but can still fail. Please checkout the OpenVINS v2.6 pull request for details. We present an open platform, termed OpenVINS, for visual-inertial estimation research for both the academic community and practitioners from industry. The open sourced codebase provides a foundation for researchers and engineers to quickly start developing new capabilities for their visual-inertial systems. This codebase has out of the box support for commonly desired visual-inertial estimation features, which include: (i) on-manifold sliding window Kalman filter, (ii) online camera intrinsic and extrinsic calibration, (iii) camera to inertial sensor time offset calibration, (iv) SLAM landmarks with different representations and consistent First-Estimates Jacobian (FEJ) treatments, (v) modular type system for state management, (vi) extendable visual-inertial system simulator, and (vii) extensive toolbox for algorithm evaluation. Moreover, we have also focused on detailed documentation and theoretical derivations to support rapid development and research, which are greatly lacked in the current open sourced algorithms. Github project page - https://github.com/rpng/open_vins Documentation - https://docs.openvins.com/ Getting started guide - https://docs.openvins.com/getting-sta... Publication reference - http://udel.edu/~pgeneva/downloads/pa... Dataset Link - https://projects.asl.ethz.ch/datasets...