FDMO: Feature Assisted Direct Monocular Odometry

摘要

Visual Odometry (VO) can be categorized as being either direct (e.g. DSO) or feature-based (e.g. ORB-SLAM). When the system is calibrated photometrically, and images are captured at high rates, direct methods have been shown to outperform feature-based ones in terms of accuracy and processing time; they are also more robust to failure in feature-deprived environments. On the downside, direct methods rely on heuristic motion models to seed an estimate of camera motion between frames; in the event that these models are violated (e.g., erratic motion), direct methods easily fail. This paper proposes FDMO (Feature assisted Direct Monocular Odometry), a system designed to complement the advantages of both direct and featured based techniques to achieve sub-pixel accuracy, robustness in feature deprived environments, resilience to erratic and large inter-frame motions, all while maintaining a low computational cost at frame-rate. Efficiencies are also introduced to decrease the computational complexity of the feature-based mapping part. FDMO shows an average of 10% reduction in alignment drift, and 12% reduction in rotation drift when compared to the best of both ORB-SLAM and DSO, while achieving significant drift (alignment, rotation & scale) reductions (51%, 61%, 7% respectively) going over the same sequences for a second loop. FDMO is further evaluated on the EuroC dataset and was found to inherit the resilience of feature-based methods to erratic motions, while maintaining the accuracy of direct methods.