Tracing Back Error Sources to Explain and Mitigate Pose Estimation Failures

TL;DR

This paper introduces a modular, uncertainty-aware framework for robotic pose estimation that identifies error sources and applies targeted mitigation, significantly improving robustness and efficiency over traditional monolithic estimators.

Contribution

The proposed framework decomposes pose estimation into failure detection, error attribution, and targeted recovery, enhancing robustness with a lightweight estimator like ICP.

Findings

Significantly improved robustness of ICP in real-world tasks

Achieved competitive performance with simpler, faster models

Effective error attribution and targeted mitigation strategies

Abstract

Robust estimation of object poses in robotic manipulation is often addressed using foundational general estimators, that aim to handle diverse error sources naively within a single model. Still, they struggle due to environmental uncertainties, while requiring long inference times and heavy computation. In contrast, we propose a modular, uncertainty-aware framework that attributes pose estimation errors to specific error sources and applies targeted mitigation strategies only when necessary. Instantiated with Iterative Closest Point (ICP) as a simple and lightweight pose estimator, we leverage our framework for real-world robotic grasping tasks. By decomposing pose estimation into failure detection, error attribution, and targeted recovery, we significantly improve the robustness of ICP and achieve competitive performance compared to foundation models, while relying on a substantially simpler and faster pose estimator.