  In this paper, we present an efficient visual SLAM system designed to tackle
both short-term and long-term illumination challenges. Our system adopts a
hybrid approach that combines deep learning techniques for feature detection
and matching with traditional backend optimization methods. Specifically, we
propose a unified convolutional neural network (CNN) that simultaneously
extracts keypoints and structural lines. These features are then associated,
matched, triangulated, and optimized in a coupled manner. Additionally, we
introduce a lightweight relocalization pipeline that reuses the built map,
where keypoints, lines, and a structure graph are used to match the query frame
with the map. To enhance the applicability of the proposed system to real-world
robots, we deploy and accelerate the feature detection and matching networks
using C++ and NVIDIA TensorRT. Extensive experiments conducted on various
datasets demonstrate that our system outperforms other state-of-the-art visual
SLAM systems in illumination-challenging environments. Efficiency evaluations
show that our system can run at a rate of 73Hz on a PC and 40Hz on an embedded
platform.
