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Where Am I? SLAM for Mobile Machines on a Smart Working Site

Xiang, Yusheng 1; Li, Dianzhao 1; Su, Tianqing; Zhou, Quan; Brach, Christine; Mao, Samuel S.; Geimer, Marcus ORCID iD icon 1
1 Institut für Fahrzeugsystemtechnik (FAST), Karlsruher Institut für Technologie (KIT)

Abstract:

The current optimization approaches of construction machinery are mainly based on internal sensors. However, the decision of a reasonable strategy is not only determined by its intrinsic signals, but also very strongly by environmental information, especially the terrain. Due to the dynamic changing of the construction site and the consequent absence of a high definition map, the Simultaneous Localization and Mapping (SLAM) offering the terrain information for construction machines is still challenging. Current SLAM technologies proposed for mobile machines are strongly dependent on costly or computationally expensive sensors, such as RTK GPS and cameras, so that commercial use is rare. In this study, we proposed an affordable SLAM method to create a multi-layer grid map for the construction site so that the machine can have the environmental information and be optimized accordingly. Concretely, after the machine passes by the grid, we can obtain the local information and record it. Combining with positioning technology, we then create a map of the interesting places of the construction site. As a result of our research gathered from Gazebo, we showed that a suitable layout is the combination of one IMU and two differential GPS antennas using the unscented Kalman filter, which keeps the average distance error lower than 2m and the mapping error lower than 1.3% in the harsh environment. ... mehr


Verlagsausgabe §
DOI: 10.5445/IR/1000152254
Veröffentlicht am 04.11.2022
Originalveröffentlichung
DOI: 10.3390/vehicles4020031
Scopus
Zitationen: 3
Dimensions
Zitationen: 2
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Fahrzeugsystemtechnik (FAST)
Publikationstyp Zeitschriftenaufsatz
Publikationsjahr 2022
Sprache Englisch
Identifikator ISSN: 2624-8921
KITopen-ID: 1000152254
Erschienen in Vehicles
Verlag MDPI
Band 4
Heft 2
Seiten 529–552
Bemerkung zur Veröffentlichung Gefördert durch den KIT-Publikationsfonds
Vorab online veröffentlicht am 27.05.2022
Schlagwörter unscented Kalman filter; localization of construction machines; smart working site; SLAM; ROS
Nachgewiesen in Scopus
Dimensions
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