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Industrial Application of 6D Pose Estimation for Robotic Manipulation in Automotive Internal Logistics 19

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Industrial Application of 6D Pose Estimation for Robotic Man...

19th IEEE International Conference on Automation Science and Engineering, CASE 2023

作者： Quentin, Philipp Knoll, Dino Goehring, Daniel Bmw Group Munich Germany Freie Universitaet Berlin Dahlem Center for Machine Learning and Robotics Berlin Germany

ISBN: (纸本)9798350320695

Despite the advances in robotics a large proportion of the of parts handling tasks in the automotive industry's internal logistics are not automated but still performed by humans. A key component to competitively automate these processes is a 6D pose estimation that can handle a large number of different parts, is adaptable to new parts with little manual effort, and is sufficiently accurate and robust with respect to industry requirements. In this context, the question arises as to the current status quo with respect to these measures. To address this we built a representative 6D pose estimation pipeline with state-of-the-art components from economically scalable real to synthetic data generation to pose estimators and evaluated it on automotive parts with regards to a realistic sequencing process. We found that using the data generation approaches, the performance of the trained 6D pose estimators are promising, but do not meet industry requirements. We reveal that the reason for this is the inability of the estimators to provide reliable uncertainties for their poses, rather than the ability of to provide sufficiently accurate poses. In this context we further analyzed how RGB- and RGB-D-based approaches compare against this background and show that they are differently vulnerable to the domain gap induced by synthetic data. © 2023 IEEE.

关键词： Automotive industry

learning Demands for Ride-pooling Services: A Case Study in Berlin

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Learning Demands for Ride-pooling Services: A Case Study in ...

IEEE Symposium on Intelligent Vehicle

作者： Martin Aleksandrov Dahlem Center for Machine Learning and Robotics Freie Universitä Berlin Berlin Germany

ISBN: (数字)9798350348811

ISBN: (纸本)9798350348828

We design a survey-and-scenario-based method for investigating the demand of real people for ride-pooling services in Berlin. We deploy it among public transport users to study where, why, how, and when they would use ride-pooling. In particular, we learn about their socio-demographic features, trip purposes, and deciding factors, as well as their preferences for service waiting times and fares. Our results inform service operators about dispatching decisions, allow for encoding user behavior in utility functions for reasoning, and enable the design of fair pricing schemes to improve service accessibility.

关键词： Legged locomotion Shared transport Intelligent vehicles Design methodology Pricing Encoding Dispatching

EP-Diffuser: An Efficient Diffusion Model for Traffic Scene Generation and Prediction via Polynomial Representations

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arXiv 2025年

作者： Yao, Yue Bouzidi, Mohamed-Khalil Goehring, Daniel Reichardt, Joerg Continental Automotive Technologies GmbH Germany Dahlem Center for Machine Learning and Robotics Freie Universitaet Berlin Germany

As the prediction horizon increases, predicting the future evolution of traffic scenes becomes increasingly difficult due to the multi-modal nature of agent motion. Most state-of-the-art (SotA) prediction models primarily focus on forecasting the most likely future. However, for the safe operation of autonomous vehicles, it is equally important to cover the distribution for plausible motion alternatives. To address this, we introduce EP-Diffuser, a novel parameter-efficient diffusion-based generative model designed to capture the distribution of possible traffic scene evolutions. Conditioned on road layout and agent history, our model acts as a predictor and generates diverse, plausible scene continuations. We benchmark EP-Diffuser against two SotA models in terms of accuracy and plausibility of predictions on the Argoverse 2 dataset. Despite its significantly smaller model size, our approach achieves both highly accurate and plausible traffic scene predictions. We further evaluate model generalization ability in an out-of-distribution (OoD) test setting using Waymo Open dataset and show superior robustness of our approach. The code and model checkpoints can be found here: https://***/continental/EP-Diffuser. © 2025, CC BY.

关键词： Polynomials

Beyond In-Distribution Performance: A Cross-Dataset Study of Trajectory Prediction Robustness

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arXiv 2025年

作者： Yao, Yue Goehring, Daniel Reichardt, Joerg Continental Automotive GmbH Germany Dahlem Center for Machine Learning and Robotics Freie Uni-versitaet Berlin Germany

We study the Out-of-Distribution (OoD) generalization ability of three SotA trajectory prediction models with comparable In-Distribution (ID) performance but different model designs. We investigate the influence of inductive bias, size of training data and data augmentation strategy by training the models on Argoverse 2 (A2) and testing on Waymo Open Motion (WO) and vice versa. We find that the smallest model with highest inductive bias exhibits the best OoD generalization across different augmentation strategies when trained on the smaller A2 dataset and tested on the large WO dataset. In the converse setting, training all models on the larger WO dataset and testing on the smaller A2 dataset, we find that all models generalize poorly, even though the model with the highest inductive bias still exhibits the best generalization ability. We discuss possible reasons for this surprising finding and draw conclusions about the design and test of trajectory prediction models and benchmarks. © 2025, CC BY.

关键词： Prediction models

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

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MaskVal: Simple but Effective Uncertainty Quantification for...

IEEE International Conference on Automation Science and Engineering (CASE)

作者： Philipp Quentin Daniel Goehring BMW Group Munich Germany Dahlem Center for Machine Learning and Robotics Freie Universitaet Berlin Berlin Germany

ISBN: (数字)9798350358513

ISBN: (纸本)9798350358520

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.

关键词： Instance segmentation Sequential analysis Uncertainty Service robots Scalability Pose estimation Rendering (computer graphics) Reliability Ensemble learning Synthetic data

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

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arXiv 2024年

作者： Quentin, Philipp Goehring, Daniel BMW Group Munich Germany Freie Universitaet Berlin Dahlem Center for Machine Learning and Robotics Berlin Germany

关键词： Robot applications

Automated multilingual detection of Pro-Kremlin propaganda in newspapers and Telegram posts

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arXiv 2023年

作者： Solopova, Veronika Popescu, Oana-Iuliana Benzmüller, Christoph Landgraf, Tim Dahlem Center for Machine Learning and Robotics Freie Universität Berlin Germany German Aerospace Center Jena Germany

The full-scale conflict between the Russian Federation and Ukraine generated an unprecedented amount of news articles and social media data reflecting opposing ideologies and narratives. These polarized campaigns have led to mutual accusations of misinformation and fake news, shaping an atmosphere of confusion and mistrust for readers worldwide. This study analyses how the media affected and mirrored public opinion during the first month of the war using news articles and Telegram news channels in Ukrainian, Russian, Romanian and English. We propose and compare two methods of multilingual automated pro-Kremlin propaganda identification, based on Transformers and linguistic features. We analyse the advantages and disadvantages of both methods, their adaptability to new genres and languages, and ethical considerations of their usage for content moderation. With this work, we aim to lay the foundation for further development of moderation tools tailored to the current conflict. © 2023, CC BY.

关键词： Automation

Industrial Application of 6D Pose Estimation for Robotic Manipulation in Automotive Internal Logistics

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arXiv 2023年

作者： Quentin, Philipp Knoll, Dino Goehring, Daniel BMW Group Munich Germany Freie Universitaet Berlin Dahlem Center for Machine Learning and Robotics Berlin Germany

Despite the advances in robotics a large proportion of the of parts handling tasks in the automotive industry’s internal logistics are not automated but still performed by humans. A key component to competitively automate these processes is a 6D pose estimation that can handle a large number of different parts, is adaptable to new parts with little manual effort, and is sufficiently accurate and robust with respect to industry requirements. In this context, the question arises as to the current status quo with respect to these measures. To address this we built a representative 6D pose estimation pipeline with state-of-the-art components from economically scalable real to synthetic data generation to pose estimators and evaluated it on automotive parts with regards to a realistic sequencing process. We found that using the data generation approaches, the performance of the trained 6D pose estimators are promising, but do not meet industry requirements. We reveal that the reason for this is the inability of the estimators to provide reliable uncertainties for their poses, rather than the ability of to provide sufficiently accurate poses. In this context we further analyzed how RGB- and RGBD-based approaches compare against this background and show that they are differently vulnerable to the domain gap induced by synthetic data. Copyright © 2023, The Authors. All rights reserved.

关键词： Automotive industry

An Empirical Bayes Analysis of Object Trajectory Representation Models

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An Empirical Bayes Analysis of Object Trajectory Representat...

International Conference on Intelligent Transportation

作者： Yue Yao Daniel Goehring Joerg Reichardt Freie Universitat Berlin Autonomous Vehicles at the Dahlem Center for Machine Learning and Robotics of the Freie Universität Berlin

Linear trajectory models provide mathematical advantages to autonomous driving applications such as motion prediction. However, linear models' expressive power and bias for real-world trajectories have not been thoroughly analyzed. We present an in-depth empirical analysis of the trade-off between model complexity and fit error in modelling object trajectories. We analyze vehicle, cyclist, and pedestrian trajectories. Our methodology estimates observation noise and prior distributions over model parameters from several large-scale datasets. Incorporating these priors can then regularize prediction models. Our results show that linear models do represent real-world trajectories with high fidelity at very moderate model complexity. This suggests the feasibility of using linear trajectory models in future motion prediction systems with inherent mathematical advantages.

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