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Kimera-VIO - Visual Inertial Odometry with SLAM capabilities and 3D Mesh generation.

  •    C++

For evaluation plots, check our jenkins server. Kimera-VIO is a Visual Inertial Odometry pipeline for accurate State Estimation from Stereo + IMU data. It can optionally use Mono + IMU data instead of stereo cameras.

VINS-Mobile - Monocular Visual-Inertial State Estimator on Mobile Phones

  •    C++

27 Jun 2017: We upgrade the pose outputs and AR rendering to 30 Hz by motion-only 3D tracking in front-end and improve the loop-closure procedure(See our technical report for detail). VINS-Mobile is a real-time monocular visual-inertial state estimator developed by members of the HKUST Aerial Robotics Group. It runs on compatible iOS devices, and provides localization services for augmented reality (AR) applications. It is also tested for state estimation and feedback control for autonomous drones. VINS-Mobile uses sliding window optimization-based formulation for providing high-accuracy visual-inertial odometry with automatic initialization and failure recovery. The accumulated odometry errors are corrected in real-time using global pose graph SLAM. An AR demonstration is provided to showcase its capability.

VINS-Mono - A Robust and Versatile Monocular Visual-Inertial State Estimator

  •    C++

VINS-Mono is a real-time SLAM framework for Monocular Visual-Inertial Systems. It uses an optimization-based sliding window formulation for providing high-accuracy visual-inertial odometry. It features efficient IMU pre-integration with bias correction, automatic estimator initialization, online extrinsic calibration, failure detection and recovery, loop detection, and global pose graph optimization, map merge, pose graph reuse, online temporal calibration, rolling shutter support. VINS-Mono is primarily designed for state estimation and feedback control of autonomous drones, but it is also capable of providing accurate localization for AR applications. This code runs on Linux, and is fully integrated with ROS. For iOS mobile implementation, please go to VINS-Mobile.

open_vins - An open source platform for visual-inertial navigation research.

  •    C++

Welcome to the OpenVINS project! The OpenVINS project houses some core computer vision code along with a state-of-the art filter-based visual-inertial estimator. The core filter is an Extended Kalman filter which fuses inertial information with sparse visual feature tracks. These visual feature tracks are fused leveraging the Multi-State Constraint Kalman Filter (MSCKF) sliding window formulation which allows for 3D features to update the state estimate without directly estimating the feature states in the filter. Inspired by graph-based optimization systems, the included filter has modularity allowing for convenient covariance management with a proper type-based state system. Please take a look at the feature list below for full details on what the system supports. ov_secondary - This is an example secondary thread which provides loop closure in a loosely coupled manner for OpenVINS. This is a modification of the code originally developed by the HKUST aerial robotics group and can be found in their VINS-Fusion repository. Here we stress that this is a loosely coupled method, thus no information is returned to the estimator to improve the underlying OpenVINS odometry. This codebase has been modified in a few key areas including: exposing more loop closure parameters, subscribing to camera intrinsics, simplifying configuration such that only topics need to be supplied, and some tweaks to the loop closure detection to improve frequency.




xivo - X Inertial-aided Visual Odometry

  •    C++

XIVO runs at 140FPS on stored data (here from a RealSense D435i sensor) or on live streams with latency of around 1-7ms, depending on the hardware. It takes as input video frames from a calibrated camera and inertial measurements from an IMU, and outputs a sparse point cloud with attribute features and 6 DOF pose of the camera. It performs auto-calibration of the relative pose between the camera and the IMU as well as the time-stamp alignment. More demos are available here, the aproach is described in this paper. XIVO does not perform post-mortem refinement (bundle adjustment, pose graph optimization), but that can be easily added as post-processing. XIVO is an open-source repository for visual-inertial odometry/mapping. It is a simplified version of Corvis [Jones et al.,Tsotsos et al.], designed for pedagogical purposes, and incorporates odometry (relative motion of the sensor platform), local mapping (pose relative to a reference frame of the oldest visible features), and global mapping (pose relative to a global frame, including loop-closure and global re-localization — this feature, present in Corvis, is not yet incorporated in XIVO).

vio - 📨 An express "endware" that takes your feelings into consideration.

  •    TypeScript

An express "endware" that takes your feelings into consideration. VIO is a small piece of the greater thing. Rather than a framework, it's more like a light-weight implementation that takes how a maintainable site structure should be like into consideration.

vins_so

  •    C++

VINS-OS is a real-time SLAM framework for Omnidirectional and/or Stereo Visual-Inertial Systems, modified from VINS-MONO. Same as VINS-MONO, it uses an optimization-based sliding window formulation for providing high-accuracy visual-inertial odometry. It features efficient IMU pre-integration with bias correction, automatic estimator initialization, online extrinsic calibration, stereo extrinsic self-calibration, failure detection and recovery. Remove the loop detection and global pose graph optimization of VINS-MONO. VINS-OS is primarily designed for state estimation and feedback control of autonomous drones equrped with a dual-fisheye omnidirectional stereo vison system. This code runs on Linux, and is fully integrated with ROS. If you use VINS-OS for your academic research, please cite at least one of our related papers.







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