RGBD Parking Finder

RGBD Parking Finder (PF) is a tool for annotating and recognizing free parking bays given an RGBD Point cloud. It is easy to extend, because it has a modular architecture.

Requirements

PF requires the following packeges to build:

• OpenCV (>= 3.0)
• Boost
• PCL (>= 1.7)
• Eigen

How to build

PF works under Linux (tested on Ubuntu 16.04) environment. We recommend a so-called out of source build which can be achieved by the following command sequence:

• mkdir build
• cd build
• cmake ../
• make -j <number-of-cores+1>

How to use

The building phase creates three executables inside the bin folder.

• planimetry
• parking_training
• bay_detector

planimetry

It creates a plan view of the point cloud as well as the selection of the parking bays.

Usage:

$ ./planimetry /path/to/point/cloud/pointcloud.ply

Select the first bay with the mouse (if the bay is not a rectangle, you can also adapt the drawed rectangle to the bay by moving the corners), then for adding the bay to the planimetry, you have to press 'a'. Once you finish to add the bays, you have to push 'q' for quitting and saving the planimetry. Ater the process, three files are created:

1. plane_view.png is the plan view image of the point cloud;
2. matrices.txt contains the rotation and the homography matrices;
3. planimetry contains the planimetry coordinates.

parking_training

It trains a classifier given a planimetry of the cloud to train.

Usage:

$ ./parking_training /path/to/config/file/config.cfg

The config file contains the following lines:

[MODE] #read/compute 
compute

[CLASSIFIER] #adaboost/svm
adaboost

[VALIDATION_PERCENTAGE]
0.25

[CLASSIFIER_FILE_SAVE]
adaboost_classifier_fpfh.xml

[FEATURES_FILE]
../data_files/PointCloud_Inglostadt_Theater_10mm/Inglostadt_Theater_10mm_features_FPFH.txt

[FEATURES_TYPE] #ESF,FPFH,PFH,VFH,ESF+PHF,ESF+FPHF
FPFH

[POINT_CLOUD]
../data/PointCloud_Inglostadt_Theater_10mm/pointcloud.ply

[PLANIMETRY_FILE]
../data_files/PointCloud_Inglostadt_Theater_10mm/planimetry.txt

[PLANIMETRY_IMAGE]
../data_files/PointCloud_Inglostadt_Theater_10mm/plane_view.png

[TRANSFORMATION_FILE]
../data_files/PointCloud_Inglostadt_Theater_10mm/matrices.txt

Where:

• [MODE]: read for reading the feature file specified in [FEATURE FILE], compute for computing the features;
• [CLASSIFIER]: svm for choosing the support vector machine classifier, adaboost for the adaboost classifier;
• [VALIDATION_PERCENTAGE]: the value represents the percentage of the training set used for validating the classifier;
• [CLASSIFIER_FILE_SAVE]: path and name of the file for saving the classifer;
• [FEATURES_FILE]: contains the path for saving the features (it is used both for reading and writing the features);
• [FEATURE_TYPE]: contains the name of the features that you want to use: ESF,FPFH,PFH,VFH,ESF+PHF,ESF+FPHF;
• [POINT_CLOUD]: path to the point cloud to label or to classify;
• [PLANIMETRY_FILE]: path to the planimetry file of the point cloud to label;
• [PLANIMETRY_IMAGE]: path to the planimetry image of the point cloud to label;
• [TRANSFORMATION_FILE]: path to the file which contains the rotation and homography matrices of the point cloud to label.

For selecting the free and the occupied bays, you have to use the buttons f for free and c for occupied. You select the rectangles by using the mouse and then you push one of the above buttons for labeling it.

The output of parking_training is:

• read mode: the classifier file specified in [CLASSIFIER_FILE_SAVE];
• compute mode: the classifier file specified in [CLASSIFIER_FILE_SAVE] and the feature file specified in [FEATURES_FILE].

Moreover, at the end of the process you obtain the error number and the accuracy of the classifier based on the validation set.

bay_detector

It detects the free bay in a new point cloud.

Usage:

$ ./bay_detector /path/to/config/file/config.cfg

The config file contains the following lines:

[CLASSIFIER] #adaboost/svm
adaboost

[CLASSIFIER_FILE_SAVE]
../data_files/PointCloud_Inglostadt_Theater_10mm/adaboost_classifier_esf.xml

[FEATURES_TYPE] #ESF,FPFH,PFH,VFH,ESF+PHF,ESF+FPHF
ESF

[POINT_CLOUD]
../data/PointCloud_Inglostadt_Theater2_10mm/pointcloud.ply

[PLANIMETRY_FILE]
../data_files/PointCloud_Inglostadt_Theater2_10mm/planimetry.txt

[PLANIMETRY_IMAGE]
../data_files/PointCloud_Inglostadt_Theater2_10mm/plane_view.png

[TRANSFORMATION_FILE]
../data_files/PointCloud_Inglostadt_Theater2_10mm/matrices.txt

Where:

• [CLASSIFIER]: svm for choosing the support vector machine classifier, adaboost for the adaboost classifier;
• [CLASSIFIER_FILE_SAVE]: path and name of the file where the classifer is saved;
• [FEATURE_TYPE]: contains the name of the features that you want to use: ESF,FPFH,PFH,VFH,ESF+PHF,ESF+FPHF;
• [POINT_CLOUD]: path to the point cloud to classify;
• [PLANIMETRY_FILE]: path to the planimetry file of the point cloud to classify;
• [PLANIMETRY_IMAGE]: path to the planimetry image of the point cloud to classify;
• [TRANSFORMATION_FILE]: path to the file which contains the rotation and homography matrices of the point cloud to classify.

The output is an image with the classified bays.

RESULTS

Inside the folders data_files/point_cloud_1/results and data_files/point_cloud_2/results you can find the results of the classification. Two point clouds are available by following these links:

• Point Cloud 1
• Point Cloud 2