PREPARE_DATA.md

IPBLab dataset

Downloading IPBLab dataset from our server:

cd ir-mcl && mkdir data && cd data
wget https://www.ipb.uni-bonn.de/html/projects/kuang2023ral/ipblab.zip
unzip ipblab.zip

For each sequence, we provide :

• seq_{id}.bag: the ROS bag format, include raw odometer reading and raw lidar reading.
• seq_{id}.json: include raw odometer reading, ground-truth poses, and raw lidar reading.
• seq_{id}_gt_pose: the ground-truth poses in TUM format (for evaluation with evo).

Besides, there are also some configuration files are provided:

• lidar_info.json: the parameters of the 2D LiDAR sensor.
• occmap.npy: the pre-built occupancy grid map.
• b2l.txt: the transformation from the lidar link to robot's base link

The final data structure should look like

data/
├── ipblab/
│   ├── loc_test/
│   │   ├── test1/
│   │   │   ├──seq_1.bag
│   │   │   ├──seq_1.json
│   │   │   ├──seq_1_gt_pose.txt
│   │   ├──b2l.txt
│   ├──lidar_info.json
│   ├──occmap.npy

There is one sequence available for the localization experiments now, the full dataset will be released after our dataset paper is published!

Intel Lab datatse, Freiburg Building 079 dataset, and MIT CSAIL dataset

Downloading these three classical indoor 2D SLAM datasets from our server:

cd ir-mcl && mkdir data && cd data
wget https://www.ipb.uni-bonn.de/html/projects/kuang2023ral/2dslam.zip
unzip 2dslam.zip

For each sequence, we provide :

• train.json: the training set which is used for mapping or train the NOF model.
• val.json: the validation set for evaluating the model during training.
• test.json: the test set for evaluating the final model.
• occmap.npy: the pre-built occupancy grid map by using training set.

The final data structure should look like

data/
├── fr079/
│   ├──occmap.npy
│   ├──train.json
│   ├──val.json
│   ├──test.json
├── intel/
│   ├──...
├── mit/
│   ├──...

Here, we provide the converted format of these dataset for ease of use. The raw data could be found on our website: 2D Laser Dataset.