TERRAIN PREDICTION WITH A LOW-COST LIDAR SENSOR FOR MOBILE ROBOTS
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.
- Location
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Deutsche Nationalbibliothek Frankfurt am Main
- Extent
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Online-Ressource
- Language
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Englisch
- Bibliographic citation
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TERRAIN PREDICTION WITH A LOW-COST LIDAR SENSOR FOR MOBILE ROBOTS ; volume:XLVIII-2/W1-2022 ; year:2022 ; pages:81-86 ; extent:6
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ; XLVIII-2/W1-2022 (2022), 81-86 (gesamt 6)
- Creator
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Edlinger, R.
Nüchter, A.
- DOI
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10.5194/isprs-archives-XLVIII-2-W1-2022-81-2022
- URN
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urn:nbn:de:101:1-2022121504334035266928
- Rights
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Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
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15.08.2025, 7:22 AM CEST
Data provider
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Associated
- Edlinger, R.
- Nüchter, A.
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