2025/06/16 by Sai Srinivas Jeevanandam, Sandeep Inuganti, Jeevanandam, Sai Srinivas +6
Computer Science · Engineering · #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Robot Manipulation and Learning #Robotic Mechanisms and Dynamics #Robotic Path Planning Algorithms #Robotics (cs.RO)
paper · pdf · doi:10.48550/arxiv.2506.13425
openalex publication_date 2025/06/16 · openalex created_date 2025/10/13 · openalex updated_date 2026/07/28
Vision-based robotic object grasping is typically investigated in the context of isolated objects or unstructured object sets in bin picking scenarios. However, there are several settings, such as construction or warehouse automation, where a robot needs to interact with a structured object formation such as a stack. In this context, we define the problem of selecting suitable objects for grasping along with estimating an accurate 6DoF pose of these objects. To address this problem, we propose a camera-IMU based approach that prioritizes unobstructed objects on the higher layers of stacks and introduce a dataset for benchmarking and evaluation, along with a suitable evaluation metric that combines object selection with pose accuracy. Experimental results show that although our method can perform quite well, this is a challenging problem if a completely error-free solution is needed. Finally, we show results from the deployment of our method for a brick-picking application in a construction scenario.