Inline Photometrically Calibrated Hybrid Visual SLAM
This work addresses a key challenge in robotics and computer vision by improving SLAM robustness for applications in dynamic environments, though it is incremental as it builds on existing hybrid SLAM methods.
The paper tackles the problem of Visual SLAM performance degradation under varying lighting conditions by integrating online photometric calibration into a hybrid direct-indirect SLAM system, resulting in state-of-the-art outperformance across multiple datasets.
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.