uoip / stereo_msckf

Python implementation of Multi-State Constraint Kalman Filter (MSCKF) for Vision-aided Inertial Navigation.

stereo_msckf

MSCKF (Multi-State Constraint Kalman Filter) is an EKF based tightly-coupled visual-inertial odometry algorithm. S-MSCKF is MSCKF's stereo version, its results on tested datasets are comparable to state-of-art methods including OKVIS, ROVIO, and VINS-MONO. This project is a Python reimplemention of S-MSCKF, the code is directly translated from official C++ implementation KumarRobotics/msckf_vio.
For algorithm details, please refer to:

• Robust Stereo Visual Inertial Odometry for Fast Autonomous Flight, Ke Sun et al. (2017)
• A Multi-State Constraint Kalman Filterfor Vision-aided Inertial Navigation, Anastasios I. Mourikis et al. (2006)

Requerements

• Python 3.6+
• numpy
• scipy
• cv2
• pangolin (optional, for trajectory/poses visualization)

Dataset

• EuRoC MAV: visual-inertial datasets collected on-board a MAV. The datasets contain stereo images, synchronized IMU measurements, and ground-truth.
  This project implements data loader and data publisher for EuRoC MAV dataset.

Run

python vio.py --view --path path/to/your/EuRoC_MAV_dataset/MH_01_easy
or
python vio.py --path path/to/your/EuRoC_MAV_dataset/MH_01_easy (no visualization)

Results

MH_01_easy

TODO

• Systemic evaluation on EuRoC and other visual-inertial datasets;
• Optimize the speed (make it 2x~3x times faster).

License

Follow license of msckf_vio