KumarRobotics/msckf_vio

Health v2 · maintenance only

Flags: no_releases no_license

Adoption not part of the score

1969 stars · 612 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

A stereo visual-inertial odometry package implementing the Multi-State Constraint Kalman Filter (MSCKF) in C++ on ROS. It fuses synchronized stereo images and IMU data to produce real-time 6DOF pose estimation, designed for fast autonomous drone flight.

Use cases

• estimate 6DOF camera/IMU pose from stereo video and IMU data
• visual odometry for drone autonomous flight
• run VIO on the EuRoC dataset
• real-time state estimation for a quadrotor without GPS
• fuse stereo cameras and IMU with a Kalman filter
• benchmark VIO algorithms on fast-flight datasets

When to choose

• you need proven, real-time stereo VIO on ROS with a well-cited MSCKF implementation
• your robot has hardware-synchronized stereo cameras and an IMU
• you want GPS-denied pose estimation for fast-moving aerial vehicles
• you have Kalibr calibration files for your camera-IMU rig

When to avoid

• you need monocular-only VIO or visual SLAM with loop closure and mapping
• you are not using ROS or cannot build a catkin package
• you need support for modern ROS 2 or recent Ubuntu versions out of the box
• your cameras and IMU are not hardware synchronized and you cannot calibrate them

Facets

library · maturity maintenance

computer-vision robotics simulation robotics autonomous-vehicles computer-vision cpp visual-inertial-odometry stereo-vision msckf ros slam state-estimation drones kalman-filter linux

Member repositories

For agents

markdown · JSON · MCP: product_card(name="KumarRobotics/msckf_vio")