YO-VIO: Robust Multi-Sensor Semantic Fusion Localization in Dynamic Indoor Environments

Abstract

Visual Simultaneous Localization and Mapping (SLAM) is widely employed in modern mobile service robots, which help robots to capture images indoors for estimating their pose. However, as part of visual SLAM, the typical visual odometry (VO) and visual-inertial odometry (VIO) systems only work in static environments. In many scenarios, they have to work in high-dynamic environments, which brings challenges to previous visual SLAM. In this paper, we proposed a novel monocular VIO for the challenging dynamic environments. Our method can make the robot locate accurately and robustly. Based on VINS-Mono, our system constructs a dynamic objects and feature points detection module. This module combines semantic object detection, multi-sensor-aided, and the geometric 3D vision constraints to remove the dynamic feature points. According to our experiments, the results demonstrate our system outperforms SOTA monocular VIO systems in accuracy and robustness, especially in high-dynamic indoor environments.