Towards Real-time Semantic RGB-D SLAM in Dynamic Environments - Robotics Institute Carnegie Mellon University

Towards Real-time Semantic RGB-D SLAM in Dynamic Environments

Tete Ji, Chen Wang, and Lihua Xie
Conference Paper, Proceedings of (ICRA) International Conference on Robotics and Automation, May, 2021

Abstract

Most of the existing visual SLAM methods heavily rely on a static world assumption and easily fail in dynamic environments. Some recent works eliminate the influence of dynamic objects by introducing deep learning-based semantic information to SLAM systems. However such methods suffer from high computational cost and cannot handle unknown objects. In this paper, we propose a real-time semantic RGB-D SLAM system for dynamic environments that is capable of detecting both known and unknown moving objects. To reduce the computational cost, we only perform semantic segmentation on keyframes to remove known dynamic objects, and maintain a static map for robust camera tracking. Furthermore, we propose an efficient geometry module to detect unknown moving objects by clustering the depth image into a few regions and identifying the dynamic regions via their reprojection errors. The proposed method is evaluated on public datasets and real-world conditions. To the best of our knowledge, it is one of the first semantic RGB-D SLAM systems that run in real-time on a low-power embedded platform and provide high localization accuracy in dynamic environments.

BibTeX

@conference{Ji-2021-128018,
author = {Tete Ji and Chen Wang and Lihua Xie},
title = {Towards Real-time Semantic RGB-D SLAM in Dynamic Environments},
booktitle = {Proceedings of (ICRA) International Conference on Robotics and Automation},
year = {2021},
month = {May},
publisher = {IEEE},
}