Mapping-related source code for the paper: Air Bumper: A Collision Detection and Reaction Framework for Autonomous MAV Navigation. For the other modules of Air Bumper, please refer to this repo.
This work has been accepted by 2024 IEEE International Conference on Robotics and Automation (ICRA).
This software is a collision-aware volumetric mapping system, which effectively calculates Occupancy Grid Maps (OGMs) and Euclidean Distance Transforms (EDTs) with GPU. The proposed system achieves real-time performance with limited onboard computational resources.
The supplementary video can be viewed here:
Please cite at least one of our papers if you use this project in your research:
@inproceedings{wang2024air,
title={Air Bumper: A Collision Detection and Reaction Framework for Autonomous MAV Navigation},
author={Wang, Ruoyu and Guo, Zixuan and Chen, Yizhou and Wang, Xinyi and Chen, Ben M},
booktitle={2024 IEEE International Conference on Robotics and Automation (ICRA)},
pages={15735--15741},
year={2024},
organization={IEEE}}
@article{chen2022gpu,
title={GPU-accelerated incremental Euclidean distance transform for online motion planning of mobile robots},
author={Chen, Yizhou and Lai, Shupeng and Cui, Jinqiang and Wang, Biao and Chen, Ben M},
journal={IEEE Robotics and Automation Letters},
volume={7},
number={3},
pages={6894--6901},
year={2022},
publisher={IEEE}}
- Any sensor outputs pointcloud
- Depth camera (Intel D435i)
- 2D LiDAR (RPLiDAR S1)
- 3D LiDAR (Livox Mid360, Ouster OS0, Velodyne VLP-16)
- Priori knowledge
- This project runs CUDA and requires a computer with Nvidia GPU. We have successfully tested this project on CUDA 9.0, 10.2, 11.3, 11.4, 11.8 and 12.0.
- Install Ubuntu with ROS. This project has been tested on Ubuntu 16.04 (ROS Kinetic), 18.04 (ROS Melodic) and 20.04 (ROS Noetic).
cuTT is a library used for faster batch EDT.
git clone https://gitee.com/jinxer000/cutt_lts.git
cd cutt_lts
rm ./build/*
make It will create the library itself:
- include/cutt.h
- lib/libcutt.a
Copy the lib file (lib/libcutt.a) into $(GIE_folder)/lib.
If it fails to compile, please modify the Makefile according to this website.
Search your computer and find the libcudadevrt.a (e.g., /usr/local/cuda-10.2/targets/x86_64-linux/lib/libcudadevrt.a).
Copy the lib file (libcudadevrt.a) into $(GIE_folder)/lib.
Download source code from github:
mkdir -p catkin_ws/src && cd catkin_ws/src
git clone https://github.com/ryrobotics/Collision-Aware-GIE-Mapping.git
cd ..
catkin build
source devel/setup.bashHere are some pre-built lib files in lib folder, you can rename it as mentioned above and try to compile.
If the compilation with pre-built files failed, you should follow the steps carefully and retry it.
Please kindly leave a star ⭐ if this software is helpful to your projects : )
Simply remap the input data in volumetric_mapper.cpp to your own sensor topics!
Remember to set use_sim_time parameter in each launch file as false in the real world.
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Turn off Rviz during the run since it will occupy a large amount of GPU resources.
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Disable both display_glb_edt and display_glb_ogm parameter. Hence the GPU hash table won't be streamed to CPU at every iteration.
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Decrease the parameter cutoff_dist to a small number (e.g., 2m).
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Turn on fast_mode parameter. It will disable wavefront A and wavefront B (please see details in our paper). If working in confined space, (e.g., Cow-Lady dataset), the accuracy is nearly the same as the original mode.
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If the local size is too large, an "invalid argument" error will be thrown due to CUDA does not support such a large thread-block.
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The parameters bucket_max and block_max has to be increased if you are doing large-scale and fine-resolution mapping. The initialization time may be longer.
Please set for_motion_planner parameter as true. It makes the current robot position valid and observed.
Our system publishes the EDT surround by the robot as CostMap.msg in the topic "cost_map". Each voxel contains visibility information and the distance value. If your motion planning package are not implemented together with GIE, then you can only access the local EDT information by subscribing to the topic "cost_map".
To access the global EDT directly, you are recommended to implement a GPU-based motion planner together with GIE-mapping. Each voxel can be retrieved by using device function get_VB_key() and get_voxID_in_VB(). In this way, the display_glb_ogm parameter can be false, saveing you tons of time.
If you are using a CPU-based planner, you can retrieve the voxel block ID like this:
int VB_idx =_hash_map->hash_table_H_std.find(blk_key)->second;And voxels inside the block can be visited with get_voxID_in_VB().
The system extracts low-level frontiers for exploration. The data type of VOXTYPE_FNT denotes the voxel that belongs to the low-level frontier. You may need to do some post-process to filter out the noise.
Developing
Revise the prebuilt map in ext_obsv.yaml. Note that pre_obs_bbx_ll and pre_obs_bbx_ur are the lower-left corner and upper-right corner of the flyable region.
Publish a pointcloud in topic "forbid_reg_cloud". If the pointcloud is 3D, please set is_ext_obsv_3D as true. Otherwise, the height of external observed obstacle is 0.2m~2.6m.
As reported in issue 1, there might be some problems in launching the mapper with Ubuntu 20.04. Please ensure that the GPU model, GPU driver version, and CUDA version match with each other. For more details, you can refer to this website. And it is recommend to activate C++ 14 in CMake when using 20.04.
Furthermore, this software is developed based on GIE-mapping, we recommend you to visit the original repo for more information and datasets to test the mapping system.