https://github.com/zju-fast-lab/opnet
Learning Unknown Space for Autonomous Navigation in Clustered Environment
https://github.com/zju-fast-lab/opnet
Last synced: over 1 year ago
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Learning Unknown Space for Autonomous Navigation in Clustered Environment
- Host: GitHub
- URL: https://github.com/zju-fast-lab/opnet
- Owner: ZJU-FAST-Lab
- Created: 2020-11-01T02:19:59.000Z (over 5 years ago)
- Default Branch: main
- Last Pushed: 2021-04-02T06:23:29.000Z (over 5 years ago)
- Last Synced: 2025-04-04T11:46:39.000Z (over 1 year ago)
- Language: C++
- Size: 24 MB
- Stars: 62
- Watchers: 5
- Forks: 14
- Open Issues: 3
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Metadata Files:
- Readme: README.md
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README
# OPNet & krrt-planner with map-prediction
Video at:
https://www.youtube.com/watch?v=Qb3ni_j0Dic
Preprint:
Learning-based 3D Occupancy Prediction for Autonomous Navigation in Occluded Environments
https://arxiv.org/abs/2011.03981
#
c++ realization of the paper: Kinodynamic RRT*: Asymptotically Optimal Motion Planning for Robots with Linear Dynamics
Building:
The depth_sensor_simulator package in uav_simulator is alternative to build with GPU or CPU to render the depth sensor measurement. By default, it is set to build with GPU in CMakeLists:
Dependencies:
1. ROS (I am using Ubuntu 16.04 and ROS Kinetic, other versions maybe also usable)
2. CUDA (I am using 10.2)
3. for branch "main" (inference using NVIDIA TensorRT):
* TensorRT 7.0.0+cuda10.2 (with TensorRT ONNX libraries)
If you want to run with only pytorch, please refer to https://gitee.com/leewlz/opnet and branch torch
4. for branch "torch" (inference using a Python node with Pytorch ):
* python > 2.7
* numpy, Ipython, tensorboardX
* Pytorch (I am using 1.3.0, later versions are also usable)
set(ENABLE_CUDA true)
Remember to change the 'arch' and 'code' flags according to your graphics card devices.
for branch "torch":
* Remember to change the model path in net_node.py -- init_param function
## Run Simulation:
1. roslaunch state_machine rviz.launch (to open rviz for visualization)
2. roslaunch state_machine bench_with_pred.launch (generate environment, start simulator)
3. roslaunch state_machine bench_with_pred.launch or bench_aggres.launch or bench_safe.launch (test)
