在线校准光照变化，提升视觉定位精度。
Inline Photometrically Calibrated Hybrid Visual SLAM
- 将光照校准融入混合式视觉定位系统，实时优化图像亮度。
- 在多个数据集上超越现有主流视觉定位方法。
- 适合需要高精度定位的自动驾驶与机器人场景。
本文提出一种融合在线序列光照校准的混合式直接-间接视觉SLAM（H-SLAM）方法。光照校准可统一不同光照条件下的像素强度，从而提升H-SLAM中直接部分的表现；同时，由于特征检测在光照变化下更稳定，间接部分也获益。所提方法在TUM monoVO等多个公开数据集及自建数据集上测试，均优于当前最先进的直接、间接和混合式视觉SLAM系统。在实际场地的在线SLAM实验中，该方法显著优于其他系统。
原文摘要 · Abstract (English)
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.
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