Direkt zum Inhalt springen
Machine Learning for Robotics
TUM School of Computation, Information and Technology
Technical University of Munich

Technical University of Munich



Informatik IX

Professorship for Machine Learning for Robotics

Smart Robotics Lab

Boltzmannstrasse 3
85748 Garching info@srl.cit.tum.de

Follow us on:


25/05/2023: RA-L Paper accepted

In our RA-L'23 work "Incremental Dense Reconstruction from Monocular Video with Guided Sparse Feature Volume Fusion" by Xingxing Zuo, Nan Yang, Nathaniel Merrill, Binbin Xu, and myself, we propose a deep feature volume-based dense reconstruction method that predicts TSDF (Truncated Signed Distance Function) values from a novel sparsified deep feature volume in real-time. An uncertainty-aware multi-view stereo (MVS) network is leveraged to estimate the physical surface locations in feature volume.

Video: https://youtu.be/bY6zffvbSGE

Paper: https://arxiv.org/abs/2305.14918

05/2023: Field Experiments in Finland

After quite a while since the last field trip, SRL went to Evo (Finland) with the DigiForest EU project team to test our autonomous drone flight stack including VI-SLAM and realtime dense mapping in the loop. Lots of fun! But also of course a lot of work.

29/03/2023: Code Release for MID-Fusion, Deep Probabilistic Feature Tracking and VIMID

We are super excited to announce that we have just released our code on object-level SLAM with MID-Fusion, Deep Probabilistic Feature Tracking and VIMID on GitHub!

Object-level SLAM aims to build a rich and accurate map of the dynamic environment by segmenting, tracking and reconstructing individual objects. It has many applications in robotics, augmented reality, autonomous driving and more.

  • MID-Fusion: An octree-based object-level multi-instance dynamic SLAM system that can provide robust camera tracking in dynamic environments and at the same time, continuously estimate geometric, semantic and motion properties for arbitrary objects in the scene.
    Video | Code
  • Deep Probabilistic Feature Tracking: A novel feature-metric tracking method that leverages deep learning to estimate probabilistic feature descriptors and uncertainties from RGB-D images. It can handle large appearance changes, occlusions and illumination variations.
    Video | Code
  • VIMID: A visual-inertial multi-instance dynamic SLAM system that incorporates inertial measurements to improve camera pose estimation and object relocalisation. It can handle fast camera motions and large-scale scenes.
    Video | Code

We hope that our code will be useful for interested researchers and practitioners and welcome any feedback, questions or suggestions!

05/03/2023: ICRA paper accepted

In our ICRA'23 work “Finding Things in the Unknown: Semantic Object-Centric Exploration with an MAV” by Sotiris Papatheodorou, Nils Funk, Dimos Tzoumanikas, Christopher Choi, Binbin Xu, and myself, we broaden the scope of autonomous exploration beyond just uncovering free space: we study the task of both finding specific objects in unknown space and reconstructing them to a target level of detail while exploring an unknown environment.

Video | Paper

12/02/2023: Lab Retreat

Greetings from our lab trip in Austria! We had interesting discussions about the future of robotics and artificial intelligence, research ethics, and many more.

15/12/2022: Sebastián Barbas Laina joining SRL@TUM

Welcome Sebastián, who is starting as a PhD student at the lab!

14/11/2022: Hanzhi Chen joining SRL@TUM

Welcome Hanzhi, who is starting as a PhD student at the lab!

02/08/2022: 3 IROS paper accepted

We are happy to announce that we will present several works at IROS 2022:

- Our recent work “Visual-Inertial SLAM with Tightly-Coupled Dropout-Tolerant GPS Fusion” by Simon Boche, Xingxing Zuo, Simon Schaefer and myself has been accepted to #IROS2022! It builds upon the realtime Visual-Inertial (VI) SLAM framework of OKVIS2 and fuses GPS measurements in a tightly-coupled approach in an underlying factor graph. We unify support for initialisation into the global coordinate frame initially or after first performing VI-SLAM only, as well as re-initialisation and alignment after potentially long GPS dropouts with respective VIO/VI-SLAM drift. Simultaneously, the framework supports VI-SLAM loop-closures at any time (whether GPS is available or not). For more information, please visit the project webpage!

- Our recent work "Visual-Inertial Multi-Instance Dynamic SLAM with Object-level Relocalisation" by Yifei Ren*, Binbin Xu*, Christopher L. Choi and myself has been accepted to IROS 2022! It builds upon our previous work MID-Fusion with integration of IMU information. Even in extremely dynamic scenes, it can robustly optimize for the camera pose, velocity, IMU biases and build a dense 3D reconstruction object-level map of the environment. In addition, when an object is lost or moved outside the camera view, our system can reliably recover its pose utilising object relocalisation. For more information, please visit the project webpage!

- Our recent work "Learning to Complete Object Shapes for Object-level Mapping in Dynamic Scenes" by Binbin Xu, Andrew J. Davison, and myself has been accepted to IROS 2022! We proposed a new object map representation that can preserve the details that have been observed in the past and simultaneously complete the missing geometry information. It stands in the middle between reconstructing object geometry from scratch and mapping using object shape priors. We achieve this by conditioning it on integrated volumetric volume and a category-level shape prior. The results show that completed object geometry leads to better object reconstruction and also better tracking accuracy. For more information, please visit the project webpage!

25/07/2022: ECCV paper accepted

Our recent work “BodySLAM: Joint Camera Localisation, Mapping, and Human Motion Tracking” was accepted to #ECCV2022! We show that a joint optimisation of camera trajectories and human position, shape, and posture improves the respective estimates when compared to estimating these separately. Furthermore, we demonstrate that we can recover accurate scale from monocular videos using our proposed human motion model. For more information, please visit the project webpage!

22/06/2022: High Schools @ SRL

Today, we have had the chance to introduce students from all over Baveria to the fascinating world of robotics, as one of five research labs at TUM. Thanks to the Baverian Academy of Sciences and Humanities for the organization!

01/05/2022: Several open positions

SRL is currently considering PhD and postdoc applicants with background in SLAM and robot/drone navigation for the following projects:

  • Digital Forestry (EU Project)
  • Spatial AI for Cooperative Construction Robotics
  • Multimodal Perception

If interested, please apply through the Chair's portal and mention your interest in any of these.

01/02/2021: Smart Robotics Lab moving to TUM

Per 1st February 2021, the Smart Robotics lab starts its move / re-start at TUM. Excited to continue our mobile robotics research!

Rechte Seite

Informatik IX

Professorship for Machine Learning for Robotics

Smart Robotics Lab

Boltzmannstrasse 3
85748 Garching info@srl.cit.tum.de

Follow us on: