Dynamic SLAM: The Need For Speed

The static world assumption is standard in most simultaneous localisation and mapping (SLAM) algorithms. Increased deployment of autonomous systems to unstructured dynamic environments is driving a need to identify moving objects and estimate their velocity in real-time. Most existing SLAM based app...

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Bibliographic Details
Published in:arXiv.org 2020-02
Main Authors: Henein, Mina, Zhang, Jun, Mahony, Robert, Viorela Ila
Format: Article
Language:English
Subjects:
Algorithms
Computer simulation
Moving object recognition
Semantic segmentation
Simultaneous localization and mapping
Three dimensional models
Online Access:Get full text
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Summary:The static world assumption is standard in most simultaneous localisation and mapping (SLAM) algorithms. Increased deployment of autonomous systems to unstructured dynamic environments is driving a need to identify moving objects and estimate their velocity in real-time. Most existing SLAM based approaches rely on a database of 3D models of objects or impose significant motion constraints. In this paper, we propose a new feature-based, model-free, object-aware dynamic SLAM algorithm that exploits semantic segmentation to allow estimation of motion of rigid objects in a scene without the need to estimate the object poses or have any prior knowledge of their 3D models. The algorithm generates a map of dynamic and static structure and has the ability to extract velocities of rigid moving objects in the scene. Its performance is demonstrated on simulated, synthetic and real-world datasets.
ISSN:2331-8422