  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.
