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)
- Python 3.6+
- numpy
- scipy
- cv2
- pangolin (optional, for trajectory/poses visualization)
- 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.
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)
MH_01_easy
- Systemic evaluation on EuRoC and other visual-inertial datasets;
- Optimize the speed (make it 2x~3x times faster).
Follow license of msckf_vio