  Feature based visual odometry and SLAM methods require accurate and fast
correspondence matching between consecutive image frames for precise camera
pose estimation in real-time. Current feature matching pipelines either rely
solely on the descriptive capabilities of the feature extractor or need
computationally complex optimization schemes. We present the lightweight
pipeline DynaMiTe, which is agnostic to the descriptor input and leverages
spatial-temporal cues with efficient statistical measures. The theoretical
backbone of the method lies within a probabilistic formulation of feature
matching and the respective study of physically motivated constraints. A
dynamically adaptable local motion model encapsulates groups of features in an
efficient data structure. Temporal constraints transfer information of the
local motion model across time, thus additionally reducing the search space
complexity for matching. DynaMiTe achieves superior results both in terms of
matching accuracy and camera pose estimation with high frame rates,
outperforming state-of-the-art matching methods while being computationally
more efficient.
