  Planar grasp detection is one of the most fundamental tasks to robotic
manipulation, and the recent progress of consumer-grade RGB-D sensors enables
delivering more comprehensive features from both the texture and shape
modalities. However, depth maps are generally of a relatively lower quality
with much stronger noise compared to RGB images, making it challenging to
acquire grasp depth and fuse multi-modal clues. To address the two issues, this
paper proposes a novel learning based approach to RGB-D grasp detection, namely
Depth Guided Cross-modal Attention Network (DGCAN). To better leverage the
geometry information recorded in the depth channel, a complete 6-dimensional
rectangle representation is adopted with the grasp depth dedicatedly considered
in addition to those defined in the common 5-dimensional one. The prediction of
the extra grasp depth substantially strengthens feature learning, thereby
leading to more accurate results. Moreover, to reduce the negative impact
caused by the discrepancy of data quality in two modalities, a Local
Cross-modal Attention (LCA) module is designed, where the depth features are
refined according to cross-modal relations and concatenated to the RGB ones for
more sufficient fusion. Extensive simulation and physical evaluations are
conducted and the experimental results highlight the superiority of the
proposed approach.
