GMC : grid based motion clustering in dynamic environment

GMC : grid based motion clustering in dynamic environment

Conventional SLAM algorithms takes a strong assumption of scene motionlessness, which limits the application in real environments. This paper tries to tackle the challenging visual SLAM issue of complicated environments. We present GMC, grid-based motion clustering approach, a lightweight dynamic ob...

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Bibliographic Details
Main Authors: Zhang, Handuo, Hasith, Karunasekera, Zhou, Hui, Wang, Han
Other Authors: School of Electrical and Electronic Engineering
Format: Conference or Workshop Item
Language:English
Published: 2020
Subjects:
Engineering::Electrical and electronic engineering
Visual SLAM
Motion Coherence
Online Access:https://hdl.handle.net/10356/141791
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Institution: Nanyang Technological University
Language: English
Description
Summary:Conventional SLAM algorithms takes a strong assumption of scene motionlessness, which limits the application in real environments. This paper tries to tackle the challenging visual SLAM issue of complicated environments. We present GMC, grid-based motion clustering approach, a lightweight dynamic object filtering method that is free from high-power and expensive processors and is able to differentiate moving objects out of the surroundings. GMC encapsulates motion consistency as the statistical likelihood of detected key points within a certain region. Using this method can we provide real-time and robust correspondence algorithm that can differentiate dynamic objects with static backgrounds. Furthermore, we evaluate our system in the public TUM dataset. To compare with the state-of-the-art methods, our system can provide more accurate results by detecting dynamic objects.

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