Abstract
The fusion of low-cost RGB and depth cameras has improved Visual Odometry’s performance significantly especially when encountering feature sparsity and rotational dynamics. However, established Red, Green, Blue, and Depth (RGB-D) methods still lose tracking or accumulate large drift when
confronted with strong feature sparsity, as is often the case in
indoor pedestrian localization.
To address this challenge we propose a novel Plane Prior that
extracts the largest planes from successive depth images and
computes a probability distribution over the camera’s rotational
movement based on the planes’ correspondence. Our method
demonstrates resilience even under extremely challenging conditions,
such as when the camera is directed towards a uniformly
textured surface, by providing crucial motion insights without
reliance on texture, multiple planes, or distinct lines. Moreover,
it remains unaffected by reflections and shadows, which are
common in indoor settings.
To demonstrate the effectiveness of our method, we integrate
our Plane Prior into the maximum likelihood optimization of
the camera pose in the Direct Visual Odometry framework. We
show on publicly available as well as self-collected pedestrian data that our Prior significantly reduces positional drift in scenarios of strong feature sparsity.
confronted with strong feature sparsity, as is often the case in
indoor pedestrian localization.
To address this challenge we propose a novel Plane Prior that
extracts the largest planes from successive depth images and
computes a probability distribution over the camera’s rotational
movement based on the planes’ correspondence. Our method
demonstrates resilience even under extremely challenging conditions,
such as when the camera is directed towards a uniformly
textured surface, by providing crucial motion insights without
reliance on texture, multiple planes, or distinct lines. Moreover,
it remains unaffected by reflections and shadows, which are
common in indoor settings.
To demonstrate the effectiveness of our method, we integrate
our Plane Prior into the maximum likelihood optimization of
the camera pose in the Direct Visual Odometry framework. We
show on publicly available as well as self-collected pedestrian data that our Prior significantly reduces positional drift in scenarios of strong feature sparsity.
