MuSe-Dataset

Overview

The objective of this project was to develop a dataset repository with sensors and provide it to the robotics community on which various algorithms can be tested and verified. The dataset presently collected and discussed in the report comprises of data collected from a single robot system for calibration and verificaton purposes.

The datasets being collected comprises of a setup of various sensors which are discussed in the upcoming chapters. The whole repository will be made available to the public in the upcoming days with the resources we used and developed during the calibration procedure and verification. The end users can either use our calibration parameters or can apply their own algorithms to find the same. Datasets provided will be in the form of ROSBAGS which is the standard way of recording the data in the ROS environment. The bags would contain all messages received from sensors and with their intrinsic and extrinsic parameters.

All the sensors have been mounted on top of a mobile robot iClebo Kobuki platform.
The data collected includes the data from the following sensors:

1. ZED Camera
2. Kinect
3. Global Shutter Camera
4. LIDAR
5. Odometry
6. Omni Camera

Calibration of LIDAR and Odometry

We have used Andrea Censi 's csm and calibration packages to perform calibration of the LIDAR and Odometry parameters. The present project an RPLIDAR A1 development kit by robot peak which contains the RPLIDAR (2D Laser Scanner).

Instructions for performing calibration:

1. Install the csm and calibration packages mentioned above.
2. Clone the repository and replace calibration/matlab_calibration_script/test_c1.bag with your bag file containing LIDAR and Odometry data.
3. Make sure that you build the package calibration/matlab_calibration_script/calibration-master.
4. Run the script calibration/matlab_calibration_script/automate_LIDAR_odom.py to get the desired calibration results.

Extract backend from Google Cartographer

1. Install Cartographer and make sure that it runs on the data for which the data is to be extracted.
2. Replace catkin_ws/src/cartographer_ros/cartographer_ros/map_builder_bridge.cc in Cartographer with cartographer/map_builder_bridge.cc provided in this repository. Run Cartographer for your data with the aforementioned changes.
3. You will get a file info.txt. Place it in the same directory as extract-backend.py provided in this directory and execute this Python script.
4. A new file isam_reordered.txt will be created which contains the required backend data.