The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLVIII-2/W1-2022
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-2/W1-2022, 81–86, 2022
https://doi.org/10.5194/isprs-archives-XLVIII-2-W1-2022-81-2022
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-2/W1-2022, 81–86, 2022
https://doi.org/10.5194/isprs-archives-XLVIII-2-W1-2022-81-2022
 
08 Dec 2022
08 Dec 2022

TERRAIN PREDICTION WITH A LOW-COST LIDAR SENSOR FOR MOBILE ROBOTS

R. Edlinger1 and A. Nüchter2 R. Edlinger and A. Nüchter
  • 1University of Applied Sciences Upper Austria, Wels, Austria
  • 2Robotics and Telematics, Julius Maximilian University of Würzburg, Germany

Keywords: 3d scanning, low-cost, terrain prediction, online terrain estimation, ToF sensors

Abstract. Terrain modelling influences various aspects of mobile robot navigation. The ability to explore in rough terrain and to recognise ground conditions are essential to perform different activities efficiently, safely and satisfactorily. For this reason, intelligent vehicles and robotic systems need cognitive capabilities to understand the terrain and derive information from it. The information is mostly acquired and processed by very high resolution 3D-cameras and LiDAR sensors which provide full 360-degree environmental view to deliver accurate 3D data. The aim of this paper is to find out whether a low-cost sensor variant can measure sufficient and significant data from the terrain in order to modify the navigation behaviour and provide the correct control commands. In this paper we describe a low-cost sensor with Infrared Time-of-Flight (ToF) technology and 64 pixel depth image. Furthermore, different experiments on the detection of the sensor were conducted and with appropriate filters and signal processing algorithms the environmental perception could be significantly improved. In summary, our results provide both evidence and guidelines for the use of the selected sensor in environmental perception to improve local obstacle detection and terrain modelling, which we believe will lead to a very cost-effective improvement in competence and situational awareness.