Enabling long-term operation during day and night for collaborative robots requires a comprehensive understanding of the unstructured environment. Besides, in the dynamic environment, robots must be able to recognize dynamic objects and collaboratively build a global map. This chapter proposes a novel approach for dynamic collaborative mapping based on multimodal environmental perception. For each mission, robots first apply heterogeneous sensor fusion model to detect humans and separate them to acquire static observations. Then, the collaborative mapping is performed to estimate the relative position between robots and local 3D maps are integrated into a globally consistent 3D map. The experiment is conducted in the day and night rainforest with moving people. The results show the accuracy, robustness, and versatility in 3D map fusion missions.
All-Weather Collaborative Mapping with Dynamic Objects
Springer Tracts in Autonomous Systems
Collaborative Perception, Localization and Mapping for Autonomous Systems ; Kapitel : 6 ; 101-116
2020-11-14
16 pages
Aufsatz/Kapitel (Buch)
Elektronische Ressource
Englisch
Online Contents | 1996
Weather data collection through incentivized and collaborative drone flights
Europäisches Patentamt | 2021
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