Title: Urban Scene Segmentation with Laser-Constrained CRFs,
Abstract: Robots typically possess sensors of different modalities, such as colour
cameras, inertial measurement units, and 3D laser scanners. Often, solving a
particular problem becomes easier when more than one modality is used. However,
while there are undeniable benefits to combine sensors of different modalities
the process tends to be complicated. Segmenting scenes observed by the robot
into a discrete set of classes is a central requirement for autonomy as
understanding the scene is the first step to reason about future situations.
Scene segmentation is commonly performed using either image data or 3D point
cloud data. In computer vision many successful methods for scene segmentation
are based on conditional random fields (CRF) where the maximum a posteriori
(MAP) solution to the segmentation can be obtained by inference. In this paper
we devise a new CRF inference method for scene segmentation that incorporates
global constraints, enforcing the sets of nodes are assigned the same class
label. To do this efficiently, the CRF is formulated as a relaxed quadratic
program whose MAP solution is found using a gradient-based optimisation
approach. The proposed method is evaluated on images and 3D point cloud data
gathered in urban environments where image data provides the appearance
features needed by the CRF, while the 3D point cloud data provides global
spatial constraints over sets of nodes. Comparisons with belief propagation,
conventional quadratic programming relaxation, and higher order potential CRF
show the benefits of the proposed method.