Visualizing lidar data

Visualizing lidar data using Uber Autonomous Visualization System (AVS) and a Jupyter Notebook Application

This project contains two different applications for visualizing lidar data using KITTI Vision Benchmark Suite datasets.

1. Uber AVS Autonomous Visualization System (AVS) --- XVIZ (the data layer for AVS)

Quick start

You need Node.js and yarn to run the examples.

# Clone XVIZ
$ git clone https://github.com/uber/xviz.git
$ cd xviz

# Install dependencies
$ yarn bootstrap

Convert and serve KITTI example data:

# Download KITTI data
$ ./scripts/download-kitti-data.sh

# Convert KITTI data if necessary and run the XVIZ Server and Client
$ ./scripts/run-kitti-example.sh

2. KITTI Dataset Exploration

Dependencies

Apart from the common dependencies like numpy and matplotlib notebook requires pykitti. You can install pykitti via pip using:

pip install pykitti

Project structure

File	Description
kitti-dataset.ipynb	Jupyter Notebook with dataset visualisation routines and output.
parseTrackletXML.py	Methods for parsing tracklets (e.g. dataset labels), originally created by Christian Herdtweck.
utilities.py	Convenient logging routines.

I have used one of the raw datasets available on KITTI website.

2011_09_26_drive_0005 (0.6 GB)

Length: 160 frames (00:16 minutes)

Image resolution: 1392 x 512 pixels

Labels: 9 Cars, 3 Vans, 0 Trucks, 2 Pedestrians, 0 Sitters, 1 Cyclists, 0 Trams, 0 Misc

References:

• Uber Xviz A protocol for real-time transfer and visualization of autonomy data - https://github.com/uber/xviz
• Alex Staravoitau Visualizing lidar data --- https://navoshta.com/kitti-lidar/