This paper presents an integrated approach to Visual SLAM, merging online sequential photometric calibration within a Hybrid direct-indirect visual SLAM (H-SLAM). Photometric calibration helps normalize pixel intensity values under different lighting conditions, and thereby improves the direct component of our H-SLAM. A tangential benefit also results to the indirect component of H-SLAM given that the detected features are more stable across variable lighting conditions. Our proposed photometrically calibrated H-SLAM is tested on several datasets, including the TUM monoVO as well as on a dataset we created. Calibrated H-SLAM outperforms other state of the art direct, indirect, and hybrid Visual SLAM systems in all the experiments. Furthermore, in online SLAM tested at our site, it also significantly outperformed the other SLAM Systems.

翻译：本文提出一种集成化的视觉SLAM方法，将在线序列光度标定与混合直接-间接视觉SLAM（H-SLAM）相融合。光度标定有助于在不同光照条件下归一化像素强度值，从而提升我们H-SLAM系统中直接法组件的性能。由于检测到的特征在变化光照条件下具有更高稳定性，H-SLAM的间接法组件也获得了附加收益。我们在多个数据集上测试了所提出的光度标定H-SLAM系统，包括TUM monoVO数据集及我们自建的数据集。实验结果表明，经过标定的H-SLAM在所有测试中均优于当前最先进的直接法、间接法及混合视觉SLAM系统。此外，在我们场地进行的在线SLAM测试中，该系统也显著超越了其他SLAM系统。