Multiposture leg tracking for temporarily vision restricted environments based on fusion of laser and radar sensor data

  • Leg tracking is an established field in mobile robotics and machine vision in general. These algorithms, however, only distinguish the scene between leg and nonleg detections. In application fields like firefighting, where people tend to choose squatting or crouching over standing postures, those methods will inevitably fail. Further, tracking based on a single sensor system may reduce the overall reliability if brought to outdoor or complex environments with limited vision on the target objectives. Therefore, we extend our recent work to a multiposture detection system based on laser and radar sensors, that are fused to allow for maximal reliability and accuracy in scenarios as complex as indoor firefighting with vastly limited vision. The proposed tracking pipeline is trained and extensively validated on a new data set. We show that the radar tracker reaches state-of-the-art performance, and that laser and fusion tracker outperform recent methods.

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Metadaten
Author:Nils MandischerORCiDGND, Ruikun Hou, Burkhard Corves
URN:urn:nbn:de:bvb:384-opus4-1178687
Frontdoor URLhttps://opus.bibliothek.uni-augsburg.de/opus4/117868
ISSN:1556-4959OPAC
ISSN:1556-4967OPAC
Parent Title (English):Journal of Field Robotics
Publisher:Wiley
Place of publication:Weinheim
Type:Article
Language:English
Year of first Publication:2023
Publishing Institution:Universität Augsburg
Release Date:2025/01/07
Volume:40
Issue:6
First Page:1620
Last Page:1638
DOI:https://doi.org/10.1002/rob.22195
Institutes:Fakultät für Angewandte Informatik
Fakultät für Angewandte Informatik / Institut für Informatik
Fakultät für Angewandte Informatik / Institut für Informatik / Lehrstuhl für Ingenieurinformatik mit Schwerpunkt Mechatronik
Dewey Decimal Classification:6 Technik, Medizin, angewandte Wissenschaften / 60 Technik / 600 Technik, Technologie
Licence (German):CC-BY-NC 4.0: Creative Commons: Namensnennung - Nicht kommerziell (mit Print on Demand)