You Only Scan Once: A Dynamic Scene Reconstruction Pipeline for 6-DoF
  Robotic Grasping of Novel Objects
Lei Zhou Haozhe Wang Zhengshen Zhang Zhiyang Liu Francis EH Tay adn Marcelo H. Ang. Jr
In the realm of robotic grasping, achieving accurate and reliable
interactions with the environment is a pivotal challenge. Traditional methods
of grasp planning methods utilizing partial point clouds derived from depth
image often suffer from reduced scene understanding due to occlusion,
ultimately impeding their grasping accuracy. Furthermore, scene reconstruction
methods have primarily relied upon static techniques, which are susceptible to
environment change during manipulation process limits their efficacy in
real-time grasping tasks. To address these limitations, this paper introduces a
novel two-stage pipeline for dynamic scene reconstruction. In the first stage,
our approach takes scene scanning as input to register each target object with
mesh reconstruction and novel object pose tracking. In the second stage, pose
tracking is still performed to provide object poses in real-time, enabling our
approach to transform the reconstructed object point clouds back into the
scene. Unlike conventional methodologies, which rely on static scene snapshots,
our method continuously captures the evolving scene geometry, resulting in a
comprehensive and up-to-date point cloud representation. By circumventing the
constraints posed by occlusion, our method enhances the overall grasp planning
process and empowers state-of-the-art 6-DoF robotic grasping algorithms to
exhibit markedly improved accuracy.