ROBI: A Multi-View Dataset for Reflective Objects in Robotic Bin-Picking

TL;DR

The paper introduces ROBI, a comprehensive dataset for 6D object pose estimation and depth fusion in robotic bin-picking, focusing on challenging reflective, texture-less objects in cluttered scenes, to advance perception algorithms.

Contribution

The paper presents the ROBI dataset, including multi-view RGB-D data and annotations for reflective objects, enabling improved evaluation and development of perception methods in robotic bin-picking.

Findings

Depth data quality degrades with highly reflective objects.

Severe occlusions and clutter pose significant challenges.

The dataset facilitates benchmarking of pose estimation and depth fusion algorithms.

Abstract

In robotic bin-picking applications, the perception of texture-less, highly reflective parts is a valuable but challenging task. The high glossiness can introduce fake edges in RGB images and inaccurate depth measurements especially in heavily cluttered bin scenario. In this paper, we present the ROBI (Reflective Objects in BIns) dataset, a public dataset for 6D object pose estimation and multi-view depth fusion in robotic bin-picking scenarios. The ROBI dataset includes a total of 63 bin-picking scenes captured with two active stereo camera: a high-cost Ensenso sensor and a low-cost RealSense sensor. For each scene, the monochrome/RGB images and depth maps are captured from sampled view spheres around the scene, and are annotated with accurate 6D poses of visible objects and an associated visibility score. For evaluating the performance of depth fusion, we captured the ground truth depth maps by high-cost Ensenso camera with objects coated in anti-reflective scanning spray. To show the utility of the dataset, we evaluated the representative algorithms of 6D object pose estimation and multi-view depth fusion on the full dataset. Evaluation results demonstrate the difficulty of highly reflective objects, especially in difficult cases due to the degradation of depth data quality, severe occlusions and cluttered scene. The ROBI dataset is available online at https://www.trailab.utias.utoronto.ca/robi.