45 lines
1.8 KiB
Plaintext
45 lines
1.8 KiB
Plaintext
/**
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@page getting-started Getting started
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Welcome to the OpenVINS project!
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The following guides will help new users through the downloading of the software and running on datasets that we support.
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Additionally, we provide information on how to get your own sensors running on our system and have a guide on how we perform calibration.
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Please feel free to open an issue if you find any missing or areas that could be clarified.
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@section highlevel High-level overview
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From a high level the system is build on a few key algorithms.
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At the center we have the ov_core which contains a lot of standard computer vision algorithms and utilities that anybody can use.
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Specifically it stores the following large components:
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- Sparse feature visual tracking (KLT and descriptor-based)
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- Fundamental math types used to represent states
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- Initialization procedures
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- Multi-sensor simulator that generates synthetic measurements
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This ov_core library is used by the ov_msckf system which contains our filter-based estimator.
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Within this we have the state, its manager, type system, prediction, and update algorithms.
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We encourage users to look at the specific documentation for a detailed view of what we support.
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The ov_eval library has a bunch of evaluation methods and scripts that one can use to generate research results for publication.
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@section getting-started-more Getting Started Guides
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- @subpage gs-installing --- Installation guide for OpenVINS and dependencies
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- @subpage dev-docker --- Installing with Docker instead of from source
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- @subpage gs-tutorial --- Simple tutorial on getting OpenVINS running out of the box.
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- @subpage gs-datasets --- Links to supported datasets and configuration files
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- @subpage gs-calibration --- Guide to how to calibration your own visual-inertial sensors.
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*/ |