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Kimera-VIO is a library for accurate state estimation from stereo or mono cameras and IMU data. It can also generate 3D meshes and perform loop closure detection with GTSAM.
VIO-mono. The average parallax of tracked features between current frame and the latest keyframe is beyond a certain threshold. To avoid rotation-only parallax, rotation compensation via IMU integration is used. The number of tracked features goes below a certain threshold. ORB-SLAM (Surval of the fittest strategy) Time from last keyframe > 0.5s.
This method, known as VIO (visual-inertial odometry), improves estimation performance when there is a lack of distinctive features in the scene to track motion visually. SLAM (simultaneous localization and mapping) is built on top of VIO, creating a map of key points that can be used to determine if an area is previously seen.
Visual inertial odometry (VIO) and simultaneous localization and mapping (SLAM) are used to simulate a user's position, movement and surroundings within a virtual world. Cameras and other sensors are able to interpret where and how an object is moving through a virtual environment.
Similar to other SLAM system, VIO can be categorized by feature-based or direct method frontend, filter or optimization-based backend, or VI-SLAM vs VIO based on whether it has the loop closure detection and build a global consistency map. In this post, we name both VI-SLAM and VIO as VIO.
Visual and Inertial Odometry and SLAM. Metric 6 degree of freedom state estimation is possible with a single camera and an inertial measurement unit. ... VIO is the only viable alternative to GPS and lidar-based odometry to achieve accurate state estimation. Since both cameras and IMUs are very cheap, these sensor types are ubiquitous in all ...
Here are basic instructions for setting up the project, there is some more detailed help included in the later sections (e.g., for Linux). Install CMake, glfw and ffmpeg, e.g., by brew install cmake glfw ffmpeg.; Clone this repository with the --recursive option (this will take a while); Build dependencies by running cd 3rdparty/mobile-cv-suite; ./scripts/build.sh
We present HybVIO, a novel hybrid approach for combining filtering-based visual-inertial odometry (VIO) with optimization-based SLAM. The core of our method is highly robust, independent VIO with improved IMU bias modeling, outlier rejection, stationarity detection, and feature track selection, which is adjustable to run on embedded hardware. Long-term consistency is achieved with a loosely ...
VIO/SLAM is mainly divided into two parts: the front end and the back end. Front end roughly estimates the motion of adjacent images as well as IMU preintegration constraint and provides a good initial value for the back end. Data Selection --- Geometry Geometry
Abstract— VIO-SLAM is a complex robotics challenge that includes calculating a vehicle's trajectory while also mapping the surroundings using a combination of visual and inertial sensors. VIO-SLAM has drawn a lot of attention recently because of the potential uses it could have in a number of ...