This package provides a minimal set of tools for working with the KITTI dataset [1] in Python. So far only the raw datasets and odometry benchmark datasets are supported, but we're working on adding support for the others. We welcome contributions from the community.

You can install pykitti via pip using

pip install pykitti

To install the package from source, simply clone or download the repository to your machine

git clone https://github.com/utiasSTARS/pykitti.git

and run the provided setup tool

cd pykitti
python setup.py install

Homogeneous coordinate transformations are provided as 4x4 numpy.array objects and are denoted as T_destinationFrame_originFrame.

Pinhole camera intrinsics for camera N are provided as 3x3 numpy.array objects and are denoted as K_camN. Stereo pair baselines are given in meters as b_gray for the monochrome stereo pair (cam0 and cam1), and b_rgb for the color stereo pair (cam2 and cam3).

More detailed examples can be found in the demos directory, but the general idea is to specify what dataset you want to load, then load the parts you need and do something with them:

import pykitti

basedir = '/your/dataset/dir'
date = '2011_09_26'
drive = '0019'

# The range argument is optional - default is None, which loads the whole dataset
data = pykitti.raw(basedir, date, drive, range(0, 50, 5))

# Data are loaded only if requested
data.load_calib()
point_cam0 = data.calib.T_cam0_velo.dot(point_velo)

data.load_oxts()
point_w = data.oxts[0].T_w_imu.dot(point_imu)

data.load_rgb()
cam2_image = data.rgb[0].left

Image data can be automatically converted to an OpenCV-friendly format (i.e., uint8 with BGR color channel ordering) simply by specifying an additional parameter in the image loader function:

data.load_gray(format='cv2')  # Loads images as uint8 grayscale
data.load_rgb(format='cv2')   # Loads images as uint8 with BGR ordering

Note: This package does not actually require that OpenCV be installed on your system, except to run demo_raw_cv2.py.

If you use this code in your research, we would appreciate if you referred to this repository (https://github.com/utiasSTARS/pykitti) in a footnote in your paper.

[1] A. Geiger, P. Lenz, C. Stiller, and R. Urtasun, "Vision meets robotics: The KITTI dataset," Int. J. Robot. Research (IJRR), vol. 32, no. 11, pp. 1231–1237, Sep. 2013. http://www.cvlibs.net/datasets/kitti/