GMC : grid based motion clustering in dynamic environment

• 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
• Institution: Nanyang Technological University

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.