A Unified Formulation for Visual Odometry
This work addresses the challenge of robust and accurate visual odometry for robotics and autonomous systems by integrating complementary paradigms, though it is incremental as it builds on existing direct and indirect methods.
The paper tackles the problem of visual odometry by proposing a unified formulation (UFVO) that tightly couples direct and indirect methods, resulting in a system that handles large inter-frame motions, achieves sub-pixel accuracy, runs in real-time, and outperforms state-of-the-art direct, indirect, and hybrid systems.
Monocular Odometry systems can be broadly categorized as being either Direct, Indirect, or a hybrid of both. While Indirect systems process an alternative image representation to compute geometric residuals, Direct methods process the image pixels directly to generate photometric residuals. Both paradigms have distinct but often complementary properties. This paper presents a Unified Formulation for Visual Odometry, referred to as UFVO, with the following key contributions: (1) a tight coupling of photometric (Direct) and geometric (Indirect) measurements using a joint multi-objective optimization, (2) the use of a utility function as a decision maker that incorporates prior knowledge on both paradigms, (3) descriptor sharing, where a feature can have more than one type of descriptor and its different descriptors are used for tracking and mapping, (4) the depth estimation of both corner features and pixel features within the same map using an inverse depth parametrization, and (5) a corner and pixel selection strategy that extracts both types of information, while promoting a uniform distribution over the image domain. Experiments show that our proposed system can handle large inter-frame motions, inherits the sub-pixel accuracy of direct methods, can run efficiently in real-time, can generate an Indirect map representation at a marginal computational cost when compared to traditional Indirect systems, all while outperforming state of the art in Direct, Indirect and hybrid systems.