Dark-SLAM: A Robust Visual Simultaneous Localization and Mapping Pipeline for an Unmanned Driving Vehicle in a Dark Night Environment

Abstract

Visual Simultaneous Localization and Mapping (VSLAM) is significant in unmanned driving, being is used to locate vehicles and create environmental maps, and provides a basis for navigation and decision making. However, in inevitable dark night environments, the SLAM system still suffers from a decline in robustness and accuracy. In this regard, this paper proposes a VSLAM pipeline called DarkSLAM. The pipeline comprises three modules: Camera Attribute Adjustment (CAA), Image Quality Enhancement (IQE), and Pose Estimation (PE). The CAA module carefully studies the strategies used for setting the camera parameters in low-illumination environments, thus improving the quality of the original images. The IQE module performs noise-suppressed image enhancement for the purpose of improving image contrast and texture details. In the PE module, a lightweight feature extraction network is constructed and performs pseudo-supervised training on low-light datasets to achieve efficient and robust data association to obtain the pose. Through experiments on low-light public datasets and real-world experiments in the dark, the necessity of the CAA and IQE modules and the parameter coupling between these modules are verified, and the feasibility of DarkSLAM is finally verified. In particular, the scene in the experiment NEU-4am has no artificial light (the illumination in this scene is between 0.01 and 0.08 lux) and the DarkSLAM achieved an accuracy of 5.2729 m at a distance of 1794.33 m.

Keywords

• autonomous driving
• VSLAM
• dark night
• machine learning