GMC: Grid Based Motion Clustering in Dynamic Environment
The demonstration of GMC filter on KITTI RAW dataset. Note that GMC can well handle dynamic object with certain size due to the grid resolution.Abstract
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.
Type
Publication
Proceedings of the 2019 SAI Intelligent Systems Conference
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