A Robust Deep Learning Enhanced Monocular SLAM System for Dynamic Environments

Abstract

Simultaneous Localization and Mapping (SLAM) has developed as a fundamental method for intelligent robot perception over the past decades. Most of the existing feature-based SLAM systems relied on traditional hand-crafted visual features and a strong static world assumption, which makes these systems vulnerable in complex dynamic environments. In this paper, we propose a robust monocular SLAM system by combining geometry-based methods with two convolutional neural networks. Specifically, a lightweight deep local feature detection network is proposed as the system front-end, which can efficiently generate keypoints and binary descriptors robust against variations in illumination and viewpoint. Besides, we propose a motion segmentation and depth estimation network for simultaneously predicting pixel-wise motion object segmentation and depth map, so that our system can easily discard dynamic features and reconstruct 3D maps without dynamic objects. The comparison against state-of-the-art methods on publicly available datasets shows the effectiveness of our system in highly dynamic environments.