Extrinsic Calibration of 3D Range Finder and Camera

For the review of submission of IROS 2017
This is a extrinsic calibration demo for 3d lidar and camera which don’t need auxiliary object or human intervention. Since the simulation data (pointcloud and image) are quite large we don’t provide the data to download but it is easy to generate by yourself with the vrep sence and ros package. More detailed experiment result at report_extrinsic.pdf

Author LIAO Qinghai
File Type compressed file(.zip)
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Reference:

Visible Light Communication-based Localization

This dataset is taken in an environment with Visible Light Communication (VLC) light beacons, for the purpose of low-cost localization using VLC.

Author Kejie Qiu, Fangyi Zhang
File Type rosbag
Topic /energy Raw light intensity signal
/map Reference map (for visualization)
/tf Transforms as groundtruth
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There are 70+ separate rosbags in the zip. The total length is over one hour.

Reference:

Use deep learning for exploration

Origin Rosbag

This dataset was taken with Microsoft Kinect on a Turtlebot.The rosbags include synchronized rgb information with depth information and control commands.

Author Shaohua Li, Lei Tai
File Type rosbag
Topic /camera/depth/image_raw Depth images
/camara/rgb/image_color Colour images
/joy Joystick control commands
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There are 10 separate rosbags in the file.

Extracted Images with labels

This dataset was the extracted images from the rosbag listed above. The text file lists the labels of control comand for every image.

Author Shaohua Li, Lei Tai
File Type depth image and labels text file
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If it helps your research, please cite our paper:

GPU Implementation of Tensor Voting for 3D data points (point-cloud)

The package provides an implementation used by ROS. It subscribes to Pointcloud2 raw point-cloud, and output PointCloud2 with featured “stick” “plate” “ball” tensor saliencies. It depends on the “ethzasl_mapping” packages, which is accessible by: https://github.com/ethz-asl

Demo codes for the basic usage and sample datasets are also provided. Download (14 Mb)

The example that independent of ROS (including CPU implementation):Download (2 Mb)

if you have problems please contact me directly: mingliu@cityu.edu.hk The updated code is supposed to be online no earlier than May, 2013. Please cite the following paper if you are interested:

Ming Liu, Francois Pomerleau, Francis Colas and Roland Siegwart, Normal Estimation for Pointcloud using GPU based Sparse Tensor Voting, IEEE International Conference on Robotics and Biomimetics(ROBIO), 2012, pdf, bibtex

Dataset of Omnidirectional Camera with Vicon Ground Truth

The dataset is taken with an omnidirection camera. The Vicon system is utilized to provide groundtruth for position in the 2D motion plane and heading with sub-mm precision. We provide two forms of the data: ROS bag and sequence of images with log for poses.

The ROS bags include the following information:

The “seq+pose log” include: unwrapped images, raw panoramic images, poses.

Clean Run (345 MB)

Still Camera with Four People (146 MB)

With Four People (258 MB)

Please use the following references for the dataset:

LibCNN

Author:Shaohua Li

A library of Convolutional Neural Network for robotic real-time applications. GitHub Link

Omnidirectional Camera Calibration Toolbox

Author:Jonas Eichenberger
This is a calibration toolbox for omnidirectional cameras which can be used with MATLAB. The most common omnidirectional camera models can be used for calibration and the toolbox has good corner extraction capabilities even for quite distorted omnidirectional images.

The toolbox is based on LIBOMNICAL.

This toolbox fixes some bugs, adds some additional features and was mainly used and tested with the Mei model. Therefore the other model implementations need probably some bug fixes too and everybody is welcome to contribute!

The idea is to have a general omnidirectional camera calibration toolbox for all the common models and a platform for future model implementations.

More Information see our GitHub

Other downloads

Usage:
./auto-bar.sh 500 target.tex

Compare current version target.tex file with version number 500 and generate a new tex file target.diff.tex. (modified from the work by Matthew Johnson.)