CHIP: A multi-sensor dataset for 6D pose estimation of chairs in industrial settings

TL;DR

CHIP is a new industrial dataset with 77,811 RGBD images capturing chairs manipulated by robots, designed to evaluate 6D pose estimation methods under realistic industrial conditions with occlusions and distractors.

Contribution

This paper introduces CHIP, the first industrial 6D pose dataset for chairs, with diverse sensors, automatic annotations, and benchmarking of zero-shot pose estimation methods.

Findings

Significant performance gaps in current methods on CHIP

Challenges due to occlusions and distractors are highlighted

Dataset will be publicly available for further research

Abstract

Accurate 6D pose estimation of complex objects in 3D environments is essential for effective robotic manipulation. Yet, existing benchmarks fall short in evaluating 6D pose estimation methods under realistic industrial conditions, as most datasets focus on household objects in domestic settings, while the few available industrial datasets are limited to artificial setups with objects placed on tables. To bridge this gap, we introduce CHIP, the first dataset designed for 6D pose estimation of chairs manipulated by a robotic arm in a real-world industrial environment. CHIP includes seven distinct chairs captured using three different RGBD sensing technologies and presents unique challenges, such as distractor objects with fine-grained differences and severe occlusions caused by the robotic arm and human operators. CHIP comprises 77,811 RGBD images annotated with ground-truth 6D poses automatically derived from the robot's kinematics, averaging 11,115 annotations per chair. We benchmark CHIP using three zero-shot 6D pose estimation methods, assessing performance across different sensor types, localization priors, and occlusion levels. Results show substantial room for improvement, highlighting the unique challenges posed by the dataset. CHIP will be publicly released.