  The reliable fusion of depth maps from multiple viewpoints has become an
important problem in many 3D reconstruction pipelines. In this work, we
investigate its impact on robotic bin-picking tasks such as 6D object pose
estimation. The performance of object pose estimation relies heavily on the
quality of depth data. However, due to the prevalence of shiny surfaces and
cluttered scenes, industrial grade depth cameras often fail to sense depth or
generate unreliable measurements from a single viewpoint. To this end, we
propose a novel probabilistic framework for scene reconstruction in robotic
bin-picking. Based on active stereo camera data, we first explicitly estimate
the uncertainty of depth measurements for mitigating the adverse effects of
both noise and outliers. The uncertainty estimates are then incorporated into a
probabilistic model for incrementally updating the scene. To extensively
evaluate the traditional fusion approach alongside our own approach, we will
release a novel representative dataset with multiple views for each bin and
curated parts. Over the entire dataset, we demonstrate that our framework
outperforms a traditional fusion approach by a 12.8% reduction in
reconstruction error, and 6.1% improvement in detection rate. The dataset will
be available at https://www.trailab.utias.utoronto.ca/robi.
