Lukas Schmid

Orcid: 0000-0002-3961-8145

Affiliations:
  • Massachusetts Institute of Technology, Department of Aeronautics and Astronautics, Cambridge, MA, USA
  • ETH Zurich, Autonomous Systems Lab, Department of Mechanical and Process Engineering, Switzerland (PhD 2022)


According to our database1, Lukas Schmid authored at least 10 papers between 2019 and 2023.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

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Bibliography

2023
Dynablox: Real-Time Detection of Diverse Dynamic Objects in Complex Environments.
IEEE Robotics Autom. Lett., October, 2023

3D VSG: Long-term Semantic Scene Change Prediction through 3D Variable Scene Graphs.
Proceedings of the IEEE International Conference on Robotics and Automation, 2023

2022
Embodied Active Domain Adaptation for Semantic Segmentation via Informative Path Planning.
IEEE Robotics Autom. Lett., 2022

Fast and Compute-Efficient Sampling-Based Local Exploration Planning via Distribution Learning.
IEEE Robotics Autom. Lett., 2022

Incremental 3D Scene Completion for Safe and Efficient Exploration Mapping and Planning.
CoRR, 2022

Panoptic Multi-TSDFs: a Flexible Representation for Online Multi-resolution Volumetric Mapping and Long-term Dynamic Scene Consistency.
Proceedings of the 2022 International Conference on Robotics and Automation, 2022

2021
A Unified Approach for Autonomous Volumetric Exploration of Large Scale Environments Under Severe Odometry Drift.
IEEE Robotics Autom. Lett., 2021

NeuralBlox: Real-Time Neural Representation Fusion for Robust Volumetric Mapping.
Proceedings of the International Conference on 3D Vision, 2021

2020
An Efficient Sampling-Based Method for Online Informative Path Planning in Unknown Environments.
IEEE Robotics Autom. Lett., 2020

2019
An Approach for Semantic Segmentation of Tree-like Vegetation.
Proceedings of the International Conference on Robotics and Automation, 2019


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