  We investigate the problem of autonomous object classification and semantic
SLAM, which in general exhibits a tight coupling between classification, metric
SLAM and planning under uncertainty. We contribute a unified framework for
inference and belief space planning (BSP) that addresses prominent sources of
uncertainty in this context: classification aliasing (classier cannot
distinguish between candidate classes from certain viewpoints), classifier
epistemic uncertainty (classifier receives data "far" from its training set),
and localization uncertainty (camera and object poses are uncertain).
Specifically, we develop two methods for maintaining a joint distribution over
robot and object poses, and over posterior class probability vector that
considers epistemic uncertainty in a Bayesian fashion. The first approach is
Multi-Hybrid (MH), where multiple hybrid beliefs over poses and classes are
maintained to approximate the joint belief over poses and posterior class
probability. The second approach is Joint Lambda Pose (JLP), where the joint
belief is maintained directly using a novel JLP factor. Furthermore, we extend
both methods to BSP, planning while reasoning about future posterior epistemic
uncertainty indirectly, or directly via a novel information-theoretic reward
function. Both inference methods utilize a novel viewpoint-dependent classifier
uncertainty model that leverages the coupling between poses and classification
scores and predicts the epistemic uncertainty from certain viewpoints. In
addition, this model is used to generate predicted measurements during
planning. To the best of our knowledge, this is the first work that reasons
about classifier epistemic uncertainty within semantic SLAM and BSP.
