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Jens Lambrecht

12 accepted papers

2024

Demonstrating Arena 3.0: Advancing Social Navigation in Collaborative and Highly Dynamic Environments

Linh Kästner, Volodymyir Shcherbyna, Huajian Zeng, Tuan Anh Le, Maximilian Ho-Kyung Schreff, Halid Osmaev +5
RSS 2024poster

Building upon our previous contributions, this paper introduces Arena 3.0, an extension of Arena-Bench, Arena 1.0, and Arena 2.0 focusing on the development, simulation, and benchmarking of social navigation approaches in collaborative environments. We significantly enhance the realism of human beha…

Cited by 8Source ↗PDF ↗code ↗Scholar ↗
2024

HabitatDyn 2.0: Dataset for Spatial Anticipation and Dynamic Object Localization

Zhengcheng Shen, Linh Kästner, Yi Gao, Jens Lambrecht
ICRA 2024poster

The ability of a robot to perceive and understand its environment is crucial for its actions and behavior. Humans are adept at using semantic information for object localization and path planning, a skill that robots need to emulate for intelligent adaptation in dynamic settings. Training of the spa…

Cited by 0Source ↗Scholar ↗
2023

Arena-Rosnav 2.0: A Development and Benchmarking Platform for Robot Navigation in Highly Dynamic Environments

Linh Kästner, Reyk Carstens, Huajian Zeng, Jacek Kmiecik, Teham Bhuiyan, Niloufar Khorsandi +2
IROS 2023

Following up on our previous works, in this paper, we present Arena-Rosnav 2.0 an extension to our previous works Arena-Bench [1] and Arena-Rosnav [2], which adds a variety of additional modules for developing and benchmarking robotic navigation approaches. The platform is fundamentally restructured

Cited by 11Source ↗code ↗Scholar ↗
2023

Demonstrating Arena-Web: A Web-based Development and Benchmarking Platform for Autonomous Navigation Approaches

Linh Kästner, Reyk Carstens, Lena Nahrwold, Christopher Liebig, Volodymyr Shcherbyna, Subhin Lee +1
RSS 2023poster

In recent years, mobile robot navigation approaches have become increasingly important due to various application areas ranging from healthcare to warehouse logistics. In particular, Deep Reinforcement Learning approaches have gained popularity for robot navigation but are not easily accessible to n…

Cited by 3Source ↗PDF ↗Scholar ↗
2022

All-in-One: A DRL-based Control Switch Combining State-of-the-art Navigation Planners

Linh KU+000E4stner, Johannes Cox, Teham Buiyan, Jens Lambrecht
ICRA 2022poster

Autonomous navigation of mobile robots is an es-sential aspect in use cases such as delivery, assistance or logistics. Although traditional planning methods are well integrated into existing navigation systems, they struggle in highly dynamic en-vironments. On the other hand, Deep-Reinforcement-Lear…

Cited by 29Source ↗code ↗Scholar ↗
2022

Arena-Bench: A Benchmarking Suite for Obstacle Avoidance Approaches in Highly Dynamic Environments

Linh Kästner, Teham Bhuiyan, Tuan Anh Le, Elias Treis, Johannes Cox, Boris Meinardus +6
RA-L 2022

The ability to autonomously navigate safely, especially within dynamic environments, is paramount for mobile robotics. In recent years, DRL approaches have shown superior performance in dynamic obstacle avoidance. However, these learning-based approaches are often developed in specially designed sim

Cited by 43Source ↗code ↗Scholar ↗
2022

Human-Following and -guiding in Crowded Environments using Semantic Deep-Reinforcement-Learning for Mobile Service Robots

Linh Kästner, Bassel Fatloun, Zhengcheng Shen, Daniel Gawrisch, Jens Lambrecht
ICRA 2022poster

Assistance robots have gained widespread attention in various industries such as logistics and human assistance. The tasks of guiding or following a human in a crowded environment such as airports or train stations to carry weight or goods is still an open problem. In these use cases, the robot is n…

Cited by 22Source ↗code ↗Scholar ↗
2021

Arena-Rosnav: Towards Deployment of Deep-Reinforcement-Learning-Based Obstacle Avoidance into Conventional Autonomous Navigation Systems

Linh Kästner, Teham Buiyan, Lei Jiao, Tuan Anh Le, Xinlin Zhao, Zhengcheng Shen +1
IROS 2021poster

Recently, mobile robots have become important tools in various industries, especially in logistics. Deep reinforcement learning emerged as an alternative planning method to replace overly conservative approaches and promises more efficient and flexible navigation. However, deep reinforcement learnin…

Cited by 47Source ↗code ↗Scholar ↗
2021

Connecting Deep-Reinforcement-Learning-based Obstacle Avoidance with Conventional Global Planners using Waypoint Generators

Linh Kästner, Xinlin Zhao, Teham Buiyan, Junhui Li, Zhengcheng Shen, Jens Lambrecht +1
IROS 2021poster

Deep Reinforcement Learning has emerged as an efficient dynamic obstacle avoidance method in highly dynamic environments. It has the potential to replace overly conservative or inefficient navigation approaches. However, integrating Deep Reinforcement Learning into existing navigation systems is sti…

Cited by 35Source ↗code ↗Scholar ↗
2021

Optimizing Keypoint-based Single-Shot Camera-to-Robot Pose Estimation through Shape Segmentation

Jens Lambrecht, Philipp Grosenick, Marvin Meusel
ICRA 2021poster

We introduce an optimization method for recent approaches on keypoint-based pose estimation of robotic manipulators utilizing monocular images. The method takes into account the segmented shape of the robot using Convolutional Neural Networks and a keypoint refinement through a set of score values.…

Cited by 13Source ↗Scholar ↗
2020

A 3D-Deep-Learning-based Augmented Reality Calibration Method for Robotic Environments using Depth Sensor Data

Linh Kästner, Vlad Catalin Frasineanu, Jens Lambrecht
ICRA 2020poster

Augmented Reality and mobile robots are gaining increased attention within industries due to the high potential to make processes cost and time efficient. To facilitate augmented reality, a calibration between the Augmented Reality device and the environment is necessary. This is a challenge when de…

Cited by 41Source ↗Scholar ↗