A 5-Point Minimal Solver for Event Camera Relative Motion Estimation
Ling Gao Hang Su Daniel Gehrig Marco Cannici Davide Scaramuzza Laurent Kneip
Event-based cameras are ideal for line-based motion estimation, since they
predominantly respond to edges in the scene. However, accurately determining
the camera displacement based on events continues to be an open problem. This
is because line feature extraction and dynamics estimation are tightly coupled
when using event cameras, and no precise model is currently available for
describing the complex structures generated by lines in the space-time volume
of events. We solve this problem by deriving the correct non-linear
parametrization of such manifolds, which we term eventails, and demonstrate its
application to event-based linear motion estimation, with known rotation from
an Inertial Measurement Unit. Using this parametrization, we introduce a novel
minimal 5-point solver that jointly estimates line parameters and linear camera
velocity projections, which can be fused into a single, averaged linear
velocity when considering multiple lines. We demonstrate on both synthetic and
real data that our solver generates more stable relative motion estimates than
other methods while capturing more inliers than clustering based on
spatio-temporal planes. In particular, our method consistently achieves a 100%
success rate in estimating linear velocity where existing closed-form solvers
only achieve between 23% and 70%. The proposed eventails contribute to a better
understanding of spatio-temporal event-generated geometries and we thus believe
it will become a core building block of future event-based motion estimation
algorithms.