1 research outputs found
Hybrid Belief Pruning with Guarantees for Viewpoint-Dependent Semantic SLAM
Semantic simultaneous localization and mapping is a subject of increasing
interest in robotics and AI that directly influences the autonomous vehicles
industry, the army industries, and more. One of the challenges in this field is
to obtain object classification jointly with robot trajectory estimation.
Considering view-dependent semantic measurements, there is a coupling between
different classes, resulting in a combinatorial number of hypotheses. A common
solution is to prune hypotheses that have a sufficiently low probability and to
retain only a limited number of hypotheses. However, after pruning and
renormalization, the updated probability is overconfident with respect to the
original probability. This is especially problematic for systems that require
high accuracy. If the prior probability of the classes is independent, the
original normalization factor can be computed efficiently without pruning
hypotheses. To the best of our knowledge, this is the first work to present
these results. If the prior probability of the classes is dependent, we propose
a lower bound on the normalization factor that ensures cautious results. The
bound is calculated incrementally and with similar efficiency as in the
independent case. After pruning and updating based on the bound, this belief is
shown empirically to be close to the original belief.Comment: 8 pages, 12 figures, accepted to IRO