Data Description¶

In this section, we will introduce the data format we use in the benchmark, and how to visualize the data easily. Next section on creation will show you how to create this format data from your own data.

Benchmark Unified Format¶

We saved all our data into PCD files, first let me introduce the PCD file format:

The important two for us are VIEWPOINT, POINTS and DATA:

• VIEWPOINT - specifies an acquisition viewpoint for the points in the dataset. This could potentially be later on used for building transforms between different coordinate systems, or for aiding with features such as surface normals, that need a consistent orientation.

  The viewpoint information is specified as a translation (tx ty tz) + quaternion (qw qx qy qz). The default value is:

  VIEWPOINT 0 0 0 1 0 0 0
  

• POINTS - specifies the number of points in the dataset.

• DATA - specifies the data type that the point cloud data is stored in. As of version 0.7, three data types are supported: ascii, binary, and binary_compressed. We saved as binary for faster reading and writing.

A Header Example¶

I directly show a example header here from 004390.pcd in KITTI sequence 00:

# .PCD v0.7 - Point Cloud Data file format
VERSION 0.7
FIELDS x y z intensity
SIZE 4 4 4 4
TYPE F F F F
COUNT 1 1 1 1
WIDTH 125883
HEIGHT 1
VIEWPOINT -15.6504 17.981 -0.934952 0.882959 -0.0239536 -0.0058903 -0.468802
POINTS 125883
DATA binary

In this 004390.pcd we have 125883 points, and the pose (sensor center) of this frame is: -15.6504 17.981 -0.934952 0.882959 -0.0239536 -0.0058903 -0.468802.

Again, all points from data frames are already transformed to the world frame and VIEWPOINT is the sensor pose.

How to read PCD files¶

In C++, we usually use PCL library to read PCD files, here is a simple example:

#include <pcl/point_cloud.h>
#include <pcl/point_types.h>

pcl::PointCloud<pcl::PointXYZI>::Ptr pcd(new pcl::PointCloud<pcl::PointXYZI>);
pcl::io::loadPCDFile<pcl::PointXYZI>("data/00/004390.pcd", *pcd);

In Python, we have a simple script to read PCD files in the benchmark code, or from my gits, you don't need to read the script in detail but use it directly.

import pcdpy3 # the script I provided
pcd_data = pcdpy3.PointCloud.from_path('data/00/004390.pcd')
pc = pcd_data.np_data[:,:3] # shape (N, 3) N: the number of point, 3: x y z
# if the header have intensity or rgb field, you can get it by:
# pc_intensity = pcd_data.np_data[:,3] # shape (N,)
# pc_rgb = pcd_data.np_data[:,3:6] # shape (N, 3)

Download benchmark data¶

We already processed the data in the benchmark, you can download the data from the following links:

Download command:

wget https://zenodo.org/api/records/10886629/files-archive.zip

# or download each sequence separately
wget https://zenodo.org/records/10886629/files/00.zip
wget https://zenodo.org/records/10886629/files/05.zip
wget https://zenodo.org/records/10886629/files/av2.zip
wget https://zenodo.org/records/10886629/files/kthcampus.zip
wget https://zenodo.org/records/10886629/files/semindoor.zip
wget https://zenodo.org/records/10886629/files/twofloor.zip

Visualize the data¶

We provide a simple script to visualize the data in the benchmark, you can find it in scripts/py/data/play_data.py. You may want to download the data and requirements first.

cd scripts/py

# download the data
wget https://zenodo.org/records/10886629/files/twofloor.zip

# https://github.com/KTH-RPL/DynamicMap_Benchmark/blob/main/scripts/py/requirements.txt
pip install -r requirements.txt

Run it:

python data/play_data.py --data_folder /home/kin/data/twofloor --speed 1 # speed 1 for normal speed, 2 for faster with 2x speed

It will pop up a window to show the point cloud data, you can use the mouse to rotate, zoom in/out, and move the view. Terminal show the help information to start/stop the play.

The axis here shows the sensor frame. The video is play in sensor-frame, so you can see the sensor move around in the video.