leo-stan / particles_detection

Code associated with 'Lidar-based detection of airborne particles for robust robot perception'

particles_detection

Dependencies

• numpy
• torch
• tensorboardX
• rospy
• easydict
• glob

Installation

Clone this repository in a ROS workspace

Files

Traditional ML approach (Random Forest)

• LidarDatasetHC.py
• train_model.py
• topic_prediction.py

Deep Learning approach

• LidarDataset.py
• train_smokenet.py
• smokenet_topic_prediction.py

Data Preparation

• bag_formatter: Select parts of data in 3D space and put a label on it
• extract_rosbags: Take in formatted bags and split in training/testing/validation sets

Steps

1. Select bags of data in bag_formatter.h
2. Run bag_formatter.cpp
3. Select formatted bags in extract_rosbags.py
4. Run extract_rosbags.py

Train

train_model.py or train_smokenet.py

with input_model = None

Evaluate

train_model.py or train_smokenet.py

with input_model = model_file_name

ROS Topic prediction

$rosparam set use_sim_time true
$roslaunch smoke_detection transforms.launch
$rosrun smoke_detection scan_formatter
$rosbag play whatever bag you want to predict
$rosrun smoke_detection topic_prediction or smokenet_topic_prediction

TODO

• Add link to dataset