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MaskVal: Simple but Effective Uncertainty Quantification for 6D Pose Estimation

Philipp Quentin, Daniel Goehring

TL;DR

The paper addresses the lack of reliable uncertainty quantification for 6D pose estimation in robotics. It proposes MaskVal, a render‑and‑compare UQ method that uses existing instance segmentation and does not modify pose estimators, outperforming ensemble approaches on synthetic and real robotic tasks. The authors introduce a formal evaluation framework for UQ in 6D pose estimation and demonstrate that MaskVal improves safe and reliable operation in industrial sequencing scenarios. The work implies that uncertainty quantification should leverage segmentation information and lightweight render‑and‑compare techniques to be practical for industry.

Abstract

For the use of 6D pose estimation in robotic applications, reliable poses are of utmost importance to ensure a safe, reliable and predictable operational performance. Despite these requirements, state-of-the-art 6D pose estimators often do not provide any uncertainty quantification for their pose estimates at all, or if they do, it has been shown that the uncertainty provided is only weakly correlated with the actual true error. To address this issue, we investigate a simple but effective uncertainty quantification, that we call MaskVal, which compares the pose estimates with their corresponding instance segmentations by rendering and does not require any modification of the pose estimator itself. Despite its simplicity, MaskVal significantly outperforms a state-of-the-art ensemble method on both a dataset and a robotic setup. We show that by using MaskVal, the performance of a state-of-the-art 6D pose estimator is significantly improved towards a safe and reliable operation. In addition, we propose a new and specific approach to compare and evaluate uncertainty quantification methods for 6D pose estimation in the context of robotic manipulation.

MaskVal: Simple but Effective Uncertainty Quantification for 6D Pose Estimation

TL;DR

The paper addresses the lack of reliable uncertainty quantification for 6D pose estimation in robotics. It proposes MaskVal, a render‑and‑compare UQ method that uses existing instance segmentation and does not modify pose estimators, outperforming ensemble approaches on synthetic and real robotic tasks. The authors introduce a formal evaluation framework for UQ in 6D pose estimation and demonstrate that MaskVal improves safe and reliable operation in industrial sequencing scenarios. The work implies that uncertainty quantification should leverage segmentation information and lightweight render‑and‑compare techniques to be practical for industry.

Abstract

For the use of 6D pose estimation in robotic applications, reliable poses are of utmost importance to ensure a safe, reliable and predictable operational performance. Despite these requirements, state-of-the-art 6D pose estimators often do not provide any uncertainty quantification for their pose estimates at all, or if they do, it has been shown that the uncertainty provided is only weakly correlated with the actual true error. To address this issue, we investigate a simple but effective uncertainty quantification, that we call MaskVal, which compares the pose estimates with their corresponding instance segmentations by rendering and does not require any modification of the pose estimator itself. Despite its simplicity, MaskVal significantly outperforms a state-of-the-art ensemble method on both a dataset and a robotic setup. We show that by using MaskVal, the performance of a state-of-the-art 6D pose estimator is significantly improved towards a safe and reliable operation. In addition, we propose a new and specific approach to compare and evaluate uncertainty quantification methods for 6D pose estimation in the context of robotic manipulation.
Paper Structure (15 sections, 15 equations, 5 figures, 4 tables)

This paper contains 15 sections, 15 equations, 5 figures, 4 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Problem Formulation and Evaluation Methodology
  4. MaskVal
  5. Experiments
  6. Datasets
  7. Implementation Details
  8. 6D Pose Estimation
  9. Uncertainty Quantification via Pose Ensemble
  10. MaskVal
  11. Metric Details for Dataset Evaluation
  12. Results on Dataset
  13. Results on Robotic Experiment
  14. Interpretation of the Results
  15. Conclusion

Figures (5)

  • Figure 1: In the figure, from left to right: (1) Object detection with instance segmentation of Mask R-CNN, (2) the projected handle object model in transformed poses of GDR-Net, and (3) comparison of instance segmentations with projected pose masks accompanied by the corresponding certainty values of MaskVal incorporating the visibility ratios.
  • Figure 2: In the figure, from left to right: photorealistic lightweight scenes (S1) from the test dataset of the antenna (1) and handle (2) and render & paste scenes (S2) of the antenna (3) and handle (4).
  • Figure 3: Violin plots of the MDE on the test set for the for the antenna and handle. The white dots within the violin plots represent the location of the median and the black bars indicate the range of the lower and upper quartiles, that enclose 75 % of the data.
  • Figure 4: Average recall (AR) and average recall uncertainty (ARU) curves for the antenna and handle on the test dataset over the MDD error threshold $e_{t} \in [0, 0.03]$ m, such that the average precision is equal to or greater than 0.99 along the entire curves. The AR* curve is the average recall curve of the unfiltered pose set, for which the above average precision condition does not apply.
  • Figure 5: Robotic setup representing an automotive sequencing process of internal logistics, showing a UR10 equipped with a Framos camera and a gripper. The goal is to sequence the antennas on the conveyor belt to the sequence container on the left.