Abstract
In response to the poor performance of Visual SLAM in low light scenes and its high requirements for real-time computing, we propose a 2D-3D fused neural network that processes time consistency with minimal computational cost for enhancing a sequence of low-light images, and design two new loss functions to guide the network to focus on enhancing corners and edges. We then integrated our method into VINS-Mono and compared it with several low-light enhancement methods. The results show that our method achieves approximately 30% higher positioning accuracy than previous researches, while also having the shortest inference time.
