Volume XLII-2/W6
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W6, 289-296, 2017
https://doi.org/10.5194/isprs-archives-XLII-2-W6-289-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 4.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W6, 289-296, 2017
https://doi.org/10.5194/isprs-archives-XLII-2-W6-289-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 4.0 License.

  24 Aug 2017

24 Aug 2017

UAV PHOTOGRAMMETRIC SOLUTION USING A RASPBERRY PI CAMERA MODULE AND SMART DEVICES: TEST AND RESULTS

M. Piras, N. Grasso, and A. Abdul Jabbar M. Piras et al.
  • Politecnico di Torino, Dip. di Ingegneria dell'Ambiente, del Territorio e delle Infrastrutture, Corso Duca degli Abruzzi 24, 10129 Torino, Italy

Keywords: UAV photogrammetry, camera self-calibration, python, Raspberry Pi, mass-market, IMU

Abstract. Nowadays, smart technologies are an important part of our action and life, both in indoor and outdoor environment. There are several smart devices very friendly to be setting, where they can be integrated and embedded with other sensors, having a very low cost.

Raspberry allows to install an internal camera called Raspberry Pi Camera Module, both in RGB band and NIR band. The advantage of this system is the limited cost (< 60 euro), their light weight and their simplicity to be used and embedded.

This paper will describe a research where a Raspberry Pi with the Camera Module was installed onto a UAV hexacopter based on arducopter system, with purpose to collect pictures for photogrammetry issue. Firstly, the system was tested with aim to verify the performance of RPi camera in terms of frame per second/resolution and the power requirement. Moreover, a GNSS receiver Ublox M8T was installed and connected to the Raspberry platform in order to collect real time position and the raw data, for data processing and to define the time reference. IMU was also tested to see the impact of UAV rotors noise on different sensors like accelerometer, Gyroscope and Magnetometer.

A comparison of the achieved results (accuracy) on some check points of the point clouds obtained by the camera will be reported as well in order to analyse in deeper the main discrepancy on the generated point cloud and the potentiality of these proposed approach. In this contribute, the assembling of the system is described, in particular the dataset acquired and the results carried out will be analysed.