FORMEC 2017

DOI: 10.15287/afr.2017.893

Determination of forest road surface roughness by Kinect depth imaging

Francesco Marinello, Andrea Rosario Proto, Giuseppe Zimbalatti, Andrea Pezzuolo, Raffaele Cavalli, Stefano Grigolato

Francesco Marinello
Department of Land, Environment, Agriculture and Forestry, University of Padova, Legnaro, Italy
Andrea Rosario Proto
Department of Agriculture, Mediterranean University of Reggio Calabria, Feo di Vito 89122, Reggio Calabria, Italy
Giuseppe Zimbalatti
Department of Agriculture, Mediterranean University of Reggio Calabria, Feo di Vito 89122, Reggio Calabria, Italy
Andrea Pezzuolo
Department of Land, Environment, Agriculture and Forestry, University of Padova, Legnaro, Italy
Raffaele Cavalli
Department of Land, Environment, Agriculture and Forestry, University of Padova, Legnaro, Italy
Stefano Grigolato
Department of Land, Environment, Agriculture and Forestry, University of Padova, Legnaro, Italy. Email: stefano.grigolato@unipd.it

Online First: November 16, 2017
Marinello, F., Rosario Proto, A., Zimbalatti, G., Pezzuolo, A., Cavalli, R., Grigolato, S. 2017. Determination of forest road surface roughness by Kinect depth imaging. Annals of Forest Research DOI:10.15287/afr.2017.893


Roughness is a dynamic property of the gravel road surface that affects safety, ride comfort as well as vehicle tyre life and maintenance costs. A rapid survey of gravel road condition is fundamental for an effective maintenance planning and definition of the intervention priorities.
Different non-contact techniques such as laser scanning, ultrasonic sensors and photogrammetry have recently been proposed to reconstruct three-dimensional topography of road surface and allow extraction of roughness metrics. The application of Microsoft Kinect™ depth camera is proposed and discussed here for collection of 3D data sets from gravel roads, to be implemented in order to allow quantification of surface roughness.
The objectives are to: i) verify the applicability of the Kinect sensor for characterization of different forest roads, ii) identify the appropriateness and potential of different roughness parameters and iii) analyse the correlation with vibrations recoded by 3-axis accelerometers installed on different vehicles. The test took advantage of the implementation of the Kinect depth camera for surface roughness determination of 4 different forest gravel roads and one well-maintained asphalt road as reference. Different vehicles (mountain bike, off-road motorcycle, ATV vehicle, 4WD car and compact crossover) were included in the experiment in order to verify the vibration intensity when travelling on different road surface conditions. Correlations between the extracted roughness parameters and vibration levels of the tested vehicles were then verified. Coefficients of determination of between 0.76 and 0.97 were detected between average surface roughness and standard deviation of relative accelerations, with higher values in the case of lighter vehicles.


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