Pixel-Wise Motion Segmentation for SLAM in Dynamic Environments

Abstract

Visual simultaneous localization and mapping (SLAM) is a key prerequisite for many mobile robotic systems. A common assumption for SLAM methods is a static environment. The interference of dynamic objects can lead to impairment of the camera pose tracking and permanent distortions of the map. This limits the use of many visual SLAM systems in real world scenarios, where dynamic environments are typical. We present a novel method for pixel-wise segmentation of dynamic image sequences based on a scene flow model estimation. We detect and eliminate outlying pixels sparsely by evaluating each pixel motion separately and maintain the most possible area of static scene background for SLAM. The evaluation with the public TUM dataset demonstrates that our proposed method outperforms other comparable state-of-the-art approaches for dynamic removal for SLAM systems.