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!

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!

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!

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.

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