255 research outputs found
GOGMA: Globally-Optimal Gaussian Mixture Alignment
Gaussian mixture alignment is a family of approaches that are frequently used
for robustly solving the point-set registration problem. However, since they
use local optimisation, they are susceptible to local minima and can only
guarantee local optimality. Consequently, their accuracy is strongly dependent
on the quality of the initialisation. This paper presents the first
globally-optimal solution to the 3D rigid Gaussian mixture alignment problem
under the L2 distance between mixtures. The algorithm, named GOGMA, employs a
branch-and-bound approach to search the space of 3D rigid motions SE(3),
guaranteeing global optimality regardless of the initialisation. The geometry
of SE(3) was used to find novel upper and lower bounds for the objective
function and local optimisation was integrated into the scheme to accelerate
convergence without voiding the optimality guarantee. The evaluation
empirically supported the optimality proof and showed that the method performed
much more robustly on two challenging datasets than an existing
globally-optimal registration solution.Comment: Manuscript in press 2016 IEEE Conference on Computer Vision and
Pattern Recognitio
Contour Context: Abstract Structural Distribution for 3D LiDAR Loop Detection and Metric Pose Estimation
This paper proposes \textit{Contour Context}, a simple, effective, and
efficient topological loop closure detection pipeline with accurate 3-DoF
metric pose estimation, targeting the urban utonomous driving scenario. We
interpret the Cartesian birds' eye view (BEV) image projected from 3D LiDAR
points as layered distribution of structures. To recover elevation information
from BEVs, we slice them at different heights, and connected pixels at each
level will form contours. Each contour is parameterized by abstract
information, e.g., pixel count, center position, covariance, and mean height.
The similarity of two BEVs is calculated in sequential discrete and continuous
steps. The first step considers the geometric consensus of graph-like
constellations formed by contours in particular localities. The second step
models the majority of contours as a 2.5D Gaussian mixture model, which is used
to calculate correlation and optimize relative transform in continuous space. A
retrieval key is designed to accelerate the search of a database indexed by
layered KD-trees. We validate the efficacy of our method by comparing it with
recent works on public datasets.Comment: 7 pages, 7 figures, accepted by ICRA 202
- …