Low-Light Enhancement Visual SLAM via Deep Deformable Power Curve Estimation

The unevenness and darkness of lighting in real environments are one of the main issues that affect the practical application of visual simultaneous localization and mapping (SLAM). To address this issue, we propose a visual SLAM algorithm based on low-light enhancement networks. First, we designed a lightweight low-light enhancement network based on deformable power curves, DPCE-Net, to adjust the pixels’ dynamic range of a low-light image. The curve is derived from power function, which considers the range, monotonicity, and differentiability of pixel values, and has a high dynamic range. DPCE-Net is trained and evaluated without reference images, effectively expanding the application scenarios of low-light enhancement. Then, combine the DPCE-Net with ORB-SLAM2 to construct a low-light enhancement SLAM (DPCE-SLAM). In the low-light image enhancement experiment, we compared with the state-of-the-art method, Enlighten-GAN, and Zero-DEC, achieving the good performance in structural similarity (SSIM), mean square error (mse), peak signal to noise ratio (PSNR), and natural image quality evaluator (NIQE) indicators. In state estimation evaluation, DPCE-SLAM can effectively increase the number of correctly matched feature points in dark environments, while also improving the robustness of state estimation.