@@ -2,9 +2,6 @@ We are pleased to share the code repository associated with our paper, “Probab
This paper has been accepted for publication in the IEEE Transactions on Intelligent Vehicles.
You can access the Early Access Version of the paper here: https://ieeexplore.ieee.org/document/10487036
Currently, our team is diligently working on cleaning and documenting the repository. In just a few days, it will be fully prepared and ready for exploration!
---
ROS Node for Ground Segmentation and Obstacle Detection
...
...
@@ -24,6 +21,7 @@ Features:
ROAD Detection: This network identifies ROAD areas (which are considered traversable).
TERRAIN and VEGETATION Classification: This network classifies TERRAIN and VEGETATION as non-traversable classes.
The neural networks were trained in Matlab, and their weights were exported in .csv files.
---
Usage Instructions:
...
...
@@ -37,13 +35,22 @@ Usage Instructions:
3. Neural Network Training: The shallow neural network training is carried out in Matlab. All the required scripts can be found in the gata_neural_net_training folder. The workflow for neural net training is as follows:
Step 1:You need a rosbag with labels (XYZIRGBL) mapped to the “ground_truth_lidar_points” topic.
Step 2:Specify the filename and path for the csv dataset that will be generated and set the param “extract_data_to_external_training_of_the_network” in the yaml file to true.
Step 3:Launch the iri_ground_segmentation.launch file and reproduce the training rosbag(s).
Step 4:Once finished, stop the iri_ground_segmentation node. The resulting dataset in csv format will be dumped to file.
Step 5:Open Matlab and navigate to the gata_neural_network. Modify the param_loader.m values to match your needs (pointing to your ros-generated dataset, selecting which classes are considered to be traversable, network layers dimensions, etc.).
Step 6:After adjusting the param_loader script, run the GATANeuralNetTraining.m script.
Step 7:Matlab will start the neural net training, opening some GUI to monitor the progress. Once you are satisfied with the results (or the max epoch number is reached), stop the training. The network will be dumped to a .csv file that is ready to be used for inference in the iri_ground_segmentation ros node.
Step 8:Try the new network by modifying the “neural_net_filename_with_global_path” value in the iri_ground_segmentation.yaml file, launch the node, and feed it with a rosbag (either annotated or not) or a LiDAR sensor.
---
We hope you find this work useful! For any questions or comments, please contact us at idelpino@iri.upc.edu. If you use this work, please remember to cite the paper.
We hope you find this work useful! For any questions or comments, please contact us at idelpino@iri.upc.edu.
If you use this work, please remember to cite the paper.
