Channel Invariant Online Visibility Enhancement for Visual SLAM in a Turbid Environment

Abstract

This paper presents a real-time and channel-invariant visibility enhancement algorithm using a hybrid image enhancement approach. The proposed method is initially motivated by an underwater visual simultaneous localization and mapping (SLAM) failure in a turbid medium. The environments studied contain various particles and are dominated by a different image degradation model. Targeting image enhancement for degraded images but not being limited to it, the proposed method provides a highly effective solution for both color and gray images with substantial improvement in the process time compared to conventional methods. The proposed method introduces a hybrid scheme of two image enhancement modules: a model-based (extensive) enhancement and a model-free (immediate) enhancement. The proposed method is validated by using simulated synthetic color images and real-world color and grayscale underwater images. Real-world validation is performed in various environments such as hazy indoor, smoky indoor, and underwater. Using the ground truth trajectory or clear images acquired from the same area but without turbidity, we evaluate the proposed visibility enhancement and camera registration improvement for a feature based (ORB-SLAM2), a direct (LSD-SLAM), and a visual underwater SLAM application.