Pengluo Wang,
University of California, San Diego, 2019
Implement visual-inertial simultaneous localization and mapping (SLAM) using Extended Kalman filter. Synchronized measurements from a high-quality IMU and a stereo camera have been provided. The data is obtained from KITTI dataset Raw data and data pre-processing has been completed. The data includes:
-
IMU Measurements: linear velocity
and angular velocity
measured in the body frame of the IMU
-
Stereo Camera Images: pixel coordinates
of detected visual features with precomputed correspondences between the left and the right camera frames.
-
Time Stamps: time stamps
in UNIX standard seconds-since-the-epoch.
-
Intrinsic Calibration: stereo baseline
and camera calibration matrix
:
-
Extrinsic Calibration: the transformation
from the IMU to left camera frame.
- Python 3.7
If you're using Conda for python environment management:
conda create -n vi_slam_env python==3.7
conda activate vi_slam_env
pip install -U pip
pip install -r requirements.txt
Run
python main.py -d 0020
Dataset 0020:
Dataset 0027:
Dataset 0042:
