Inline Photometrically Calibrated Hybrid Visual SLAM

TL;DR

This paper introduces a hybrid visual SLAM system that integrates online photometric calibration to enhance accuracy and robustness across varying lighting conditions, outperforming existing methods in multiple datasets.

Contribution

The paper presents a novel hybrid visual SLAM approach that incorporates real-time photometric calibration, improving feature stability and system performance under different lighting environments.

Findings

Outperforms state-of-the-art SLAM systems in multiple datasets

Significantly improves accuracy in variable lighting conditions

Enhances feature stability across different lighting environments

Abstract

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.