Awesome

IBISCape: A Simulated Benchmark for multi-modal SLAM Systems Evaluation in Large-scale Dynamic Environments

Citation

Are you interested to use IBISCape data acquisition frameworks, or to evaluate your new SLAM system on IBISCape benchmark in an academic work ! Thanks for citing the following paper:

@Article{IBISCape22,
author={Soliman, Abanob and Bonardi, Fabien and Sidib{\'e}, D{\'e}sir{\'e} and Bouchafa, Samia},
title={{IBISCape}: A Simulated Benchmark for multi-modal {SLAM} Systems Evaluation in Large-scale Dynamic Environments},
journal={Journal of Intelligent {\&} Robotic Systems},
year={2022},
month={Oct},
day={19},
volume={106},
number={3},
pages={53},
issn={1573-0409},
doi={10.1007/s10846-022-01753-7},
url={https://doi.org/10.1007/s10846-022-01753-7}
}

To run any of our acquisition frameworks, make sure you have:

• CARLA installed version up to 0.9.11 CARLA installation repository.

IBISCape frameworks are tested on CARLA versions: 0.9.10, 0.9.10.1, and best performance with 0.9.11.

• OpenCV any version OpenCV installation repository.
• Clone our IBISCape repository in a folder next to your CARLA installation directory and check the (.egg) file imported in all the frameworks to be similar to your installation Check this link.

Calibration Acquisition frameworks

• Our new Calibration targets added to CARLA-Town3 can be downloaded from this link.
• Add our CARLA calibration town to the Map content folder to your CARLA installation directory /your_CARLA_directory/carla/Unreal/CarlaUE4/Content/Carla/Maps/.
• Then, open the CARLA-Unreal Engine interface and press Compile, Build and finally Play.
• Run one of our 6 data acquisition frameworks: (full_sensor_setup), (rgb_depth), (imu), (lidar_svo), (stereo_dvs_imu), and (stereo_rgb_imu).

$ python3 framework_name.py

• We performed the stereo RGB-IMU calibration using kalibr, and all the configuration .yaml files are added to the yaml_config_files folder. (Also, configuration files for EVO, ESVO, BASALT and ORB-SLAM3)

The calibration data acquisition is done manually, where the user can collect sequences with all the movements specific to his application.

VI-SLAM Acquisition frameworks

• Play your desired CARLA-town, then run one of our 5 data acquisition frameworks: (full_sensor_setup), (rgb_depth), (lidar_svo), (stereo_dvs_imu), and (stereo_rgb_imu).

$ python3 framework_name.py

• All sensors readings in the stereo-RGB are timestamped with the world tick, while for the mono-RGB the readings are timestamped according to the sensor local tick.
• To add people and cars to the scene, run the spawn_npc.py script in a new terminal and then close it ctrl+c. This will spawn people and cars to your Carla-client as additional actors.
• The 43 calibration and SLAM sequences for all available CARLA-towns in highly dynamic weather can be downloaded from these links: calibration, SLAM+Evaluation, and Configuration.

The VIO-SLAM data acquisition is done using CARLA autopilot, following its traffic manager to perfectly simulate the real world.

IBISCape ROS_tools

• Here we developed some python scripts to help in creating ROS bags for ROS-based calibration and odometry frameworks with the right and synchronized timestamping.
• Also, some additional scripts to read the rosbags, convert all RGB images in sequence folder to grayscale images, and associating the RGB with the corresponding depth maps.