APRL Similar Labs
Reference labs and venues for students exploring SLAM, robot perception, spatial AI, and autonomous navigation.
How to Use This Page
Read recent papers from the following labs to understand the research landscape around APRL. Focus on the problem definitions, datasets, experimental setups, and limitations rather than only the final performance numbers.
Recommended Venues
Labs to Follow
- Ayoung Kim, Seoul National University
- Cyrill Stachniss, University of Bonn
- Maurice Fallon, University of Oxford
- Javier Civera, Universidad de Zaragoza
- Fu Zhang, The University of Hong Kong
- Shaojie Shen, Hong Kong University of Science and Technology
- Tim Barfoot, University of Toronto
- Niko Suenderhauf, Queensland University of Technology
- Chen Wang, University at Buffalo
- Michael Kaess, Carnegie Mellon University
- Sebastian Scherer, Carnegie Mellon University
- Xieyuanli Chen, National University of Defense Technology
- Xuesu Xiao, George Mason University, USA
Suggested Reading Lens
- What robot perception or navigation problem is being defined?
- Which assumptions make the method work, and where might they fail?
- What sensors, environments, and robot platforms are used?
- How are mapping, localization, learning, and decision-making connected?
- How can visual-language navigation systems interact with humans through instructions, social context, and feedback?
- What would be a meaningful next experiment or extension?