Multi-object detection and pose estimation in 3D point clouds: A fast grid-based Bayesian Filter.

We address the problem of object detection and pose estimation using 3D dense data in a multiple object library scenario. State-of-the-art object detection and pose estimation methods are able cope with background clutter and occlusion with acceptable noise levels in the single object scenario. However, with multiple object libraries, even moderate amount of noise lead to frequent object identity switches and serious pose estimation errors. To attenuate these effects, we propose a joint object-id and pose filtering approach using grid-based Recursive Bayesian Filters (RBF). The grid method considers as state variables the object label and its pose, and models the dynamics of the filter with two “inertia” parameters: one for the object label and the other for the object pose. Sensor noise characteristics are taken into account with an observation noise parameter. To allow real-time functionality we propose a selective update approach that dynamically reduces the set of hypotheses evaluated at run time. We present results in realistic scenarios and compare our approach with state-of-the-art approaches in a three object problem, with significant performance improvements.